Student perspectives: How can we do data science without all of our data?

A post by Daniel Williams, Compass PhD student.

Imagine that you are employed by Chicago’s city council, and are tasked with estimating where the mean locations of reported crimes are in the city. The data that you are given only goes up to the city’s borders, even though crime does not suddenly stop beyond this artificial boundary. As a data scientist, how would you estimate these centres within the city? Your measurements are obscured past a very complex border, so regular methods such as maximum likelihood would not be appropriate.

Chicago Homicides
Figure 1: Homicides in the city of Chicago in 2008. Left: locations of each homicide. Right: a density estimate of the same crimes, highlighting where the ‘hotspots’ are.

This is an example of a more general problem in statistics named truncated probability density estimation. How do we estimate the parameters of a statistical model when data are not fully observed, and are cut off by some artificial boundary?

The Problem

Incorporation of this boundary constraint into estimation is not straightforward. There are many factors which must be considered which make regular methods infeasible, we require:

  • knowledge of the normalising constant in a statistical model, which is unknown in a truncated setting;
  • incorporation of weighting points that are more ‘affected’ by the truncation.

A statistical model (a probability density function involving some parameters \theta) is defined as:

p(x; \theta) = \frac{1}{Z(\theta)} \bar{p}(x;\theta).

This is made up of two components:  \bar{p}(x; \theta), the unnormalised part of the model, which is known analytically; and Z(\theta), the normalising constant, which is oftentimes unknown or undefinable. The goal of estimation is to find a vector of parameters \theta which characterises our model, and makes the model resemble the data as closely as possible. Usually, we can estimate \theta by trying to minimise the difference between the model, p(x;\theta), and the theoretical data density, q(x).

A Recipe for a Solution

Ingredient #1: Score Matching

A novel estimation method called score matching enables estimation even when the normalising constant, Z(\theta), is unknown. Score matching begins by taking the derivative of the logarithm of the probability density function, i.e.,

\nabla_x \log p(x; \theta) = \nabla_x \log \bar{p}(x;\theta),

which has become known as the score function. When taking the derivative, the dependence on Z(\theta) is removed. To estimate the parameter vector \theta, we can minimise the difference between q(x) and p(x;\theta) by minimising the difference between the two score functions, \nabla_x \log p(x; \theta) and \nabla_x \log q(x). One such distance measure is the expected squared distance, so that score matching aims to minimise

\mathbb{E} [\| \nabla_x \log p(x; \theta) - \nabla_x \log q(x)\|_2^2].

With this first ingredient, we have eliminated the requirement that we must know the normalising constant.

Ingredient #2: A Weighting Function

Heuristically, we can imagine our weighting function should vary with how close a point is to the boundary. To satisfy score matching assumptions, we require that this weighting function g(x) must have the property that g(x') = 0 for any point x' on the boundary. A natural candidate would be the Euclidean distance from a point x to the boundary, i.e.,

g(x) = \|x - \tilde{x}\|_2, \qquad \tilde{x} = \text{argmin}_{x'\text{ in boundary}} \|x - x'\|_2.

This easily satisfies our criteria. The distance is going to be exactly zero on the boundary itself, and will approach zero the closer the points are to the edges.

Ingredient #3: Any Statistical Model

Since we do not require knowledge of the normalising constant through the use of score matching, we can choose any probability density function p(x; \theta) that is appropriate for our data. For example, if we are modelling count data, we may choose a Poisson distribution. If we have some sort of centralised location data, such as in the Chicago crime example in the introduction, we may choose a multivariate Normal distribution.

Combine Ingredients and Mix

We aim to minimise the expected distance between the score functions, and we weight this by our function g(x), to give

\min_{\theta} \mathbb{E} [g(x) \| \nabla_x \log p(x; \theta) - \nabla_x \log q(x)\|_2^2].

The only unknown in this equation now is the data density q(x) (if we knew the true data density, there would be no point in estimating it with p(x;\theta)). However, we can rewrite this equation using integration by parts as

\min_{\theta} \mathbb{E} \big[ g(x) [\|\nabla_x \log p(x; \theta)\|_2^2 + 2\text{trace}(\nabla_x^2 \log p(x; \theta))] + 2 \nabla_x g(x)^\top \nabla_x \log p(x;\theta)\big].

This can be numerically minimised, or if p(x; \theta) is in the exponential family, analytically minimised to obtain estimates for \theta.

Results

Artificial Data

As a sanity check for testing estimation methods, it is often reassuring to perform some experiments on simulated data before moving to real world applications. Since we know the true parameter values, it is possible to calculate how far our estimated values are from their true counterparts, thus giving a way to measure estimation accuracy.

Pictured below in Figure 2 are two experiments where data are simulated from a multivariate normal distribution with mean \mu = [1, 1] and known variance \sigma^2 = 1. Our aim is to estimate the parameter \theta to be close to [1,1]. The red crosses in the image are the true means at [1,1], and the red dots are the estimates given by truncated score matching.

Figure 2 (a): Points are only visible around a unit ball from [0,0].
Figure 2 (b): Points are only visible inside a box to the left of the mean.

These figures clearly indicate that in this basic case, this method is giving sensible results. Even by ignoring most of our data, as long as we formulate our problem correctly, we can still get accurate results for estimation.

Chicago Crime

Now we come back to the motivating problem, and the task is to estimate the centres of police reported crime in the city of Chicago, given the longitudes and latitudes of homicides in 2008. Our specification changes somewhat:

  • from the original plots, it seems like there are two centres, so the statistical model we choose is a 2-dimensional mixed multivariate Normal distribution;
  • the variance is no longer known, but to keep estimation straightforward, the standard deviation \sigma is fixed so that 2\sigma roughly covers the ‘width’ of the city.

Figure 3 below shows applying this method to this application.

Figure 3: Estimates of two crime centres in Chicago for truncated score matching (truncSM) and maximum likelihood estimation (MLE).

Truncated score matching has placed its estimates for the means in similar, but slightly different places than standard MLE. Whereas the maximum likelihood estimates are more central to the truncated dataset, the truncated score matching estimates are placed closer to the outer edges of the city. For the upper-most crime centre, around the city border the data are more ‘tightly packed’ – which is a property we expect of multivariate normal data. This result does not reflect the true likelihood of crime in a certain neighbourhood, and has only been presented to provide a visual explanation of the method. The actual likelihood depends on various characteristics in the city that are not included in our analysis, see here for more details.

Final Thoughts

Score matching is a powerful tool, and by not requiring any form of normalising constant, enables the use of some more complicated models. Truncated probability density estimation is just one such example of an intricate problem which can be solved with this methodology, but it is one with far reaching and interesting applications. Whilst this blog post has focused on location-based data and estimation, truncated probability density estimation could have a range of applications. For example, consider disease/virus modelling such as the COVID-19 pandemic. The true number of cases is obscured by the number of tests that can be performed, so the density evolution of the pandemic through time could be fully modelled with incorporation of this constraint using this method. Other applications as well as extensions to this method will be fully investigated in my future PhD projects.

Some Notes

Alternative methods which do not require the normalising constant: We could choose a model which has truncation built into its specification, such as the truncated normal distribution. For complicated regions of truncation, such as the city of Chicago, it is highly unlikely that an appropriate model specification is available. We could also perform some rejection algorithm to approximate the normalising constant, such as MCMC or ABC, but these are often very slow, and do not provide an exact solution. Score matching can have an analytical estimate for exponential family models, and even when it does not, minimisation is usually straightforward and fast.

Choice of the weighting function: We did not need to specify g(x) before formulating our score matching objective function in ingredient #3. In fact, under some assumptions (the function g(x) is 1-Lipschitz continuous) it can be shown that the choice of g(x) which gives the supremum of the score matching expectation is in fact the Euclidean distance which was specified earlier, so it was not just a heuristic!

Score matching derivation conditions: This post has hand-waved some conditions of the derivation of the score matching objective function. There are two conditions which must be satisfied in the weighting function g(x) so that the integration by parts ‘trick’ can work correctly. Firstly, g(x) must be positive for every x. Secondly, for any x' in the boundary, g(x') = 0. For more information, see the further reading section below.

Further Reading

If you are interested to learn more about any of these topics, check out any of the papers below:

Estimation of Non-Normalized Statistical Models by Score Matching, Aapo Hyvärinen (2005)

Estimating Density Models with Truncation Boundaries using Score Matching. Song Liu, Takafumi Kanamori, & Daniel J. Williams (2021) 

And the Github page where all the plots and results were generated can be found here.

Student Perspectives: Embedding probability distributions in RKHSs

A post by Jake Spiteri, Compass PhD student.

Recent advancements in kernel methods have introduced a framework for nonparametric statistics by embedding and manipulating probability distributions in Hilbert spaces. In this blog post we will look at how to embed marginal and conditional distributions, and how to perform probability operations such as the sum, product, and Bayes’ rule with embeddings.

Embedding marginal distributions

Throughout this blog post we will make use of reproducing kernel Hilbert spaces (RKHS). A reproducing kernel Hilbert space is simply a Hilbert space with some additional structure, and a Hilbert space is just a topological vector space equipped with an inner product, which is also complete.

We will frequently refer to a random variable X which has domain \mathcal{X} endowed with the \sigma-algebra \mathcal{B}_\mathcal{X}.

Definition. A reproducing kernel Hilbert space \mathcal{H} on \mathcal{X} with associated positive semi-definite kernel function k: \mathcal{X} \times \mathcal{X} \rightarrow \mathbb{R} is a Hilbert space of functions f: \mathcal{X} \rightarrow \mathbb{R}. The inner product \langle\cdot,\cdot\rangle_\mathcal{H} satisfies the reproducing property: \langle f, k(x, \cdot) \rangle_\mathcal{H}, \forall f \in \mathcal{H}, x \in \mathcal{X}. We also have k(x, \cdot) \in \mathcal{H}, \forall x \in \mathcal{X}.

It may be helpful to think of \mathcal{H} as \overline{\text{span}\{k(x, \cdot)\}}; all functions in our space may be written as a sum of feature maps k(x, \cdot). This can help us visualise what functions belong to our RKHS. We should also note that kernel functions are not limited to \mathcal{X} = \mathbb{R}^d — kernel functions have been defined for inputs such as strings, graphs, images, etc.

Definition. Let \mathcal{P}(\mathcal{X}) the space of all probability measures on \mathcal{X}. We can embed any probability distribution \mathbb{P} \in \mathcal{P}(\mathcal{X}) into the RKHS \mathcal{H} with associated kernel k via the kernel mean embedding:

\mu_\mathbb{P}: \mathcal{P}(\mathcal{X}) \rightarrow \mathcal{H}, \quad \mu_\mathbb{P}: \mathbb{P} \mapsto \int k(x, \cdot) d\mathbb{P}(x)

The embedding \mu_\mathbb{P} exists and belongs to the RKHS \mathcal{H} if \mathbb{E}_\mathbb{P}[\sqrt{k(X,X)}] < \infty. The kernel mean embedding is representer of expectation in the RKHS: it satisfies the reproducing property \mathbb{E}_\mathbb{P}[f(X)] = \langle f, \mu_\mathbb{P} \rangle_\mathcal{H}, \forall f \in \mathcal{H}. I.e. can compute expectations of functions f \in \mathcal{H} with respect to \mathbb{P} by taking an inner product between f and \mu_\mathbb{P}.

A useful property of the kernel mean embedding is that it captures all characteristics of a probability distribution, for a good choice of kernel. Provided that the feature mapping induced by the kernel is injective, the embedding is unique. A kernel which corresponds to an injective feature mapping is said to be characteristic. That is, for \mathbb{P}, \mathbb{Q} \in \mathcal{P}(\mathcal{X}), if \mu_\mathbb{P} = \mu_\mathbb{Q} then we must have \mathbb{P} = \mathbb{Q} for a characteristic kernel. Choosing an appropriate kernel function for a given problem is not straightfoward, and it is common to use a ‘default’ kernel choice such as the Gaussian or Laplace kernel, as they are characteristic. We should note here that whilst the mapping from distribution to kernel mean embedding is unique, reconstructing the original density function is non-trivial.

Empirical estimator. In practice it is unlikely that we would have access to the distribution \mathbb{P}, and so rather than compute the kernel mean embedding \mu_\mathbb{P}, we can only compute its empirical estimate. Given an i.i.d. sample \{x_1, \dots, x_n\} from \mathbb{P}, we can compute the unbiased estimate \hat{\mu}_\mathbb{P} := \frac{1}{n} \sum_{i=1}^n k(x_i, \cdot). This empirical estimator has \sqrt{n}-consistency.

Above we have introduced a quantity which makes use of the mean feature mapping in the RKHS. Similarly, we may also make use of the covariance of feature mappings in the RKHS.

Definition. Let (X, Y) be a random variable taking values on domain \mathcal{X} \times \mathcal{Y}, and let \mathcal{H} and \mathcal{G} be RKHSs with kernel functions k and l on domains \mathcal{X} and \mathcal{Y} respectively. Let \phi(x) := k(x, \cdot) and \varphi(y) := l(y, \cdot) be the feature maps corresponding to kernels k and l respectively. Then we define the (uncentered) cross-covariance operator \mathcal{C}_{YX}: \mathcal{H} \rightarrow \mathcal{G} as

\mathcal{C}_{YX} := \mathbb{E}_{\mathbb{P}_{YX}}[\varphi(Y) \otimes \phi(X)],

where \otimes denotes the tensor product, and \mathbb{P}_{YX} denotes the joint probability distribution of (X, Y).

The cross-covariance operator exists if \mathbb{E}_X[k(X, X)] < \infty and \mathbb{E}_Y[l(Y, Y)] < \infty. It is useful to note that \mathcal{C}_{YX} can be identified as the kernel mean embedding of the joint distribution in the tensor product space \mathcal{G} \otimes \mathcal{H}. We emphasise that the cross-covariance operator is an operator mapping from one RKHS to another. We see that when acting on an element f \in \mathcal{H} we have \mathcal{C}_{YX}(f)= \mathbb{E}_{\mathbb{P}_{YX}}[\varphi(Y) \langle f, \phi(X) \rangle_\mathcal{H}]\in\mathcal{G}.

Empirical estimator. Given an i.i.d. sample \{x_i, y_i\}_{i=1}^n  from the joint probability distribution \mathbb{P}_{XY}, we have the following empirical estimator of the cross-covariance

\hat{\mathcal{C}}_{YX} = \frac{1}{n} \Phi^T \Upsilon,

where \Phi = [ \phi(x_1), \dots, \phi(x_n)] \in \mathcal{H}^n and \Upsilon = [ \varphi(y_1), \dots, \varphi(y_n)] \in \mathcal{G}^n are row vectors of functions in \mathcal{H} and \mathcal{G} respectively.

Embedding conditional distributions

Using the cross-covariance operator, we can now define the conditional mean embedding. We want to extend the notion of embedding a marginal distribution P(X) to that of embedding a conditional distribution P(Y | X) and P(Y | X = x) for some x \in \mathcal{X}. Embeddings which capture the dependence between variables will allow us to develop more complex algorithms, and will allow us to produce kernel versions of elementary probability operations such as the sum rule and product rule.

We start by specifying two conditions which we want the conditional mean embedding to satisfy based on our definiton of the kernel mean embedding. We will use \mu_{Y | X=x} \in \mathcal{G} and \mathcal{U}_{Y|X}:\mathcal{H} \rightarrow \mathcal{G} to denote the embeddings of the conditional distributions P(Y | X = x) and P(Y | X) respectively.

These conditions are

  1. \mu_{Y | X=x} = \mathbb{E}_{Y | X=x}[\varphi(Y) | X = x] = \mathcal{U}_{Y|X} k(x, \cdot), \quad \forall x\in\mathcal{X}.
  2. \mathbb{E}_{Y | X=x}[g(Y) | X = x] = \langle g, \mu_{Y|X=x} \rangle_\mathcal{G}, \quad \forall g \in \mathcal{G}

Note that for a given x \in \mathcal{X} the embedding of P(Y | X = x), \mu_{Y | X=x}, is an element in \mathcal{G}, whereas the embedding of P(Y | X), \mathcal{U}_{Y|X}, is an operator which traces out a family of elements in the RKHS \mathcal{G}.

It is shown in (Song, 2009) that the following definition satisfies the above properties.

Definition. Let \mathcal{C}_{XX}: \mathcal{H} \rightarrow \mathcal{H}, and \mathcal{C}_{YX}: \mathcal{H} \rightarrow \mathcal{G}. Then we can define the conditional mean embeddings of the conditional distributions P(Y | X) and P(Y | X = x), for x \in \mathcal{X}, as

\mathcal{U}_{Y|X} := \mathcal{C}_{YX} \mathcal{C}_{XX}^{-1}, \quad \mu_{Y | X=x} := \mathcal{C}_{YX} \mathcal{C}_{XX}^{-1} k(x, \cdot),

under the assumption that \mathbb{E}_{Y|X}[g(Y) | X] \in \mathcal{H}. It is easy to produce examples in which this does not hold, and so we often replace the above inverse with a Tikhonov-regularised inverse, and consider the embeddings to be approximations of the true embeddings.

Empirical estimator. Given a set of i.i.d. samples \{x_i, y_i\}_{i=1}^n from the joint distribution P(X,Y), the conditional mean embedding can be estimated using

\hat{\mu}_{Y|X=x} = \sum_{i=1}^n \beta_i l(y_i, \cdot),

where \beta := (K + n \lambda I)^{-1} k_x \in \mathbb{R}^n for K\in\mathbb{R}^{n \times n} the Gram matrix associated with \{ x_1, \dots, x_n\}, and k_x = [k(x, x_1), \dots, k(x, x_n)], and \lambda a regularisation parameter.

An example. Here we look at a simple example of the conditional mean embedding. We generate data in a method inspired by Schuster et al. (2020). We start by drawing a circle on the (x,y)-plane, and specifying 50 equidistant points on the circle. Each of these points is the mean of a Gaussian distribution over Y, and we draw 50 samples from each distribution with equal probability. The conditional distribution P(Y | X) in this scenario is a mixture of Gaussians. The top left plot of Figure 1 shows the data sampled from these Gaussians in grey, with conditional distributions overlaid in blue. The conditional mean embedding embeds each conditional distribution in the RKHS \mathcal{G} illustrated in the top right plot, via \mu_{Y|X=x} = \mathcal{U}_{Y|X} \phi(x). In the second row of Figure 1 we see three examples of the reconstructed conditional densities in red. These reconstructed densities have been obtained via the nonparametric estimator proposed in Kanagawa et al. (2014).

Figure 1. A visualisation of the conditional mean embedding.

Operations: the Sum, Product, and Bayes’ rule

Using the conditional mean embedding and the cross-covariance operator, we can produce versions of the sum, product, and Bayes’ rule which allow us to manipulate distributions embedded in an RKHS.

Recall the sum rule and product rule: P(X) = \sum_{Y \in \mathcal{Y}} P(X, Y), and P(X, Y) = P(Y | X) P(X).

Kernel Sum rule. The sum rule allows us to compute the marginal density of a variable X given the joint distribution over variables X and Y, by marginalising out Y.

Using the law of total expectation, we have \mu_X = \mathbb{E}_{XY}[\varphi(X)] = \mathbb{E}_Y \mathbb{E}_{X | Y}[\varphi(X)|Y]. Thus, we have

\mu_X = \mathcal{U}_{X|Y} \mathbb{E}_Y [\phi(Y)] = \mathcal{U}_{X|Y} \mu_Y.

Kernel Product rule. To construct a kernel product rule, we consider the tensor product feature map \varphi(X) \otimes \phi(Y). We can factorise the embedding \mu_{XY} = \mathbb{E}_{XY}[\varphi(X) \otimes \phi(Y)] in two ways using the law of total expectation

  1. \mathbb{E}_Y \mathbb{E}_{X | Y} [\varphi(X) \otimes \phi(Y) | Y] = \mathcal{U}_{X | Y} \mathbb{E}_Y[\phi(Y) \otimes \phi(Y)],
  2. \mathbb{E}_X \mathbb{E}_{Y | X} [\phi(Y) \otimes \varphi(X)| X] = \mathcal{U}_{Y | X} \mathbb{E}_X[\varphi(X) \otimes \varphi(X)].

Let \mu_Y^\otimes := \mathbb{E}_Y[\phi(Y) \otimes \phi(Y)], and \mu_X^\otimes := \mathbb{E}_X[\varphi(X) \otimes \varphi(X)]. Then we can rewrite the above as

\mu_{XY} = \mathcal{U}_{X | Y} \mu_Y^\otimes = \mathcal{U}_{Y | X}\mu_X^\otimes.

This looks similar to the sum rule, but we see that there is an additional copy of the variables Y and X passed as input, which is not marginalised out as is done in the sum rule.

Kernel Bayes rule. The kernel Bayes’ rule (KBR) is a nonparametric method which allows for Bayesian inference in the absence of a parametric model or likelihood. In KBR we embed the prior and likelihood in an RKHS via the kernel mean embedding and cross-covariance operator respectively, and use the sum and product rules to manipulate the embeddings in the RKHS.

The presentation of KBR shown here is that given in Muandet et al. (2017), which provides a concise summary of the original work (Fukumizu et al., 2013). Our aim is to compute the embedding of the posterior P(Y|X) given a prior distribution \Pi(Y). We obtain the embedding of the posterior distribution as \mu_{Y | X=x} = \mathcal{U}_{Y | X}^\Pi \varphi(x), where we use a superscript \Pi to denote dependence on the prior \Pi(Y).  More precisely, we have

\mu_{Y | X=x} = \mathcal{U}_{Y | X}^\Pi \varphi(x) = \mathcal{C}_{YX}^\Pi (\mathcal{C}_{XX}^\Pi)^{-1} \varphi(x),

where the cross-covariance operators depend on the prior, and are given by

\mathcal{C}_{YX}^\Pi = (\mathcal{U}_{X | Y} \mathcal{C}_{YY}^\Pi)^{T}, \quad \mathcal{C}_{XX}^\Pi = \mathcal{U}_{(XX) | Y} \mu_Y^\Pi.

These results follow from the product and sum rule respectively, where we have replaced the input feature map \varphi(X) with the tensor product feature map \varphi(X) \otimes \varphi(X). The embeddings \mu_Y^\Pi and \mathcal{C}_{YY}^\Pi are simply the embedded prior distribution corresponding to the feature maps \phi(Y) and \phi(Y) \otimes \phi(Y).

A KBR example. Here we look at a simple example of Bayesian inference without a likelihood, similar to that given in Fukumizu et al. (2013). In this toy example we assume that we cannot write down the likelihood function, but we can sample from it. We generate data from a Normal distribution with mean \mu = 25, and we assume the variance \sigma^2 = 2.5^2 is known. We place a Normal prior on \mu with mean equal to the mean of the observed data, and standard deviation 4. Figure 2 below shows two ways of approaching posterior inference: in the first row we use the fact that the prior and likelihood are conjugate, and we derive the analytical posterior distribution; in the second row we embed the prior distribution and the joint distribution in the RKHS and use KBR to obtain an embedding of the posterior.

Figure 2. Top row: Posterior inference when the prior and likelihood are conjugate. Bottom row: Posterior inference via KBR when the likelihood is intractable.

References

Fukumizu, K., L. Song, and A. Gretton (2013). “Kernel Bayes’ rule: Bayesian inference with positive definite kernels”. In: The Journal of Machine Learning Research 14.1, pp. 3753–3783.
Kanagawa, M. and K. Fukumizu (2014). “Recovering distributions from Gaussian RKHS embeddings”. In: Artificial Intelligence and Statistics. PMLR, pp. 457–465.
Muandet, K., K. Fukumizu, B. Sriperumbudur, and B. Schölkopf (2017). “Kernel Mean Embedding of Distributions: A Review and Beyond”. In: Foundations and Trends in Machine Learning.
Schuster, I., M. Mollenhauer, S. Klus, and K. Muandet (2020). “Kernel conditional density operators”. In: International Conference on Artificial Intelligence and Statistics. PMLR, pp. 993–1004.
Song, L., J. Huang, A. Smola, and K. Fukumizu (2009). “Hilbert space embeddings of conditional distributions with applications to dynamical systems”. In: Proceedings of the 26th Annual International Conference on Machine
Learning, pp. 961–968.

Student Perspectives: Change in the air: Tackling Bristol’s nitrogen oxide problem

A post by Dom Owens, PhD student on the Compass programme.


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“Air pollution kills an estimated seven million people worldwide every year” – World Health Organisation

Many particulates and chemicals are present in the air in urban areas like Bristol, and this poses a serious risk to our respiratory health. It is difficult to model how these concentrations behave over time due to the complex physical, environmental, and economic factors they depend on, but identifying if and when abrupt changes occur is crucial for designing and evaluating public policy measures, as outlined in the local Air Quality Annual Status Report.  Using a novel change point detection procedure to account for dependence in time and space, we provide an interpretable model for nitrogen oxide (NOx) levels in Bristol, telling us when these structural changes occur and describing the dynamics driving them in between.

Model and Change Point Detection

We model the data with a piecewise-stationary vector autoregression (VAR) model:

In between change points the time series \boldsymbol{Y}_{t}, a d-dimensional vector, depends on itself linearly over p \geq 1 previous time steps through parameter matrices \boldsymbol{A}_i^{(j)}, i=1, \dots, p with intercepts \boldsymbol{\mu}^{(j)}, but at unknown change points k_j, j = 1, \dots, q the parameters switch abruptly. \{ \boldsymbol{\varepsilon}_{t} \in \mathbb{R}^d : t \geq 1 \} are white noise errors, and we have n observations.

We phrase change point detection as a test of the hypotheses H_0: q = 0 vs. H_1: q \geq 1, i.e. the null states there is no change, and the alternative supposes there are possibly multiple changes. To test this, we use moving sum (MOSUM) statistics T_k(G) extracted from the model; these compare the scaled difference between prediction errors for G steps before and after k; when k is in a stationary region, T_k(G)  will be close to zero, but when k is near or at a change point, T_k(G)  will be large. If the maximum of these exceeds a threshold suggested by the distribution under the null, we reject H_0 and estimate change point locations with local peaks of T_k(G).

Air Quality Data

Bristol air quality map

 

With data from Open Data Bristol over the period from January 2010 to March 2021, we have hourly readings of NOx levels at five locations (with Site IDs) around the city of Bristol, UK: AURN St Paul’s (452); Brislington Depot (203); Parson Street School (215); Wells Road (270); Fishponds Road (463). Taking daily averages, we control for meteorological and seasonal effects such as temperature, wind speed and direction, and the day of the week, with linear regression then analyse the residuals.

 

Residual series with estimated change points in red

 

We use the bandwidth G=280 days to ensure estimation accuracy relative to the number of parameters, which effectively asserts that two changes cannot occur in a shorter time span. The MOSUM procedure rejects the null hypothesis and detects ten changes, pictured above. We might attribute the first few to the Joint Local Transport Plan which began in 2011, while the later changes may be due to policy implemented after a Council supported motion in November 2016. The image below visualises the estimated parameters around the change point in November 2016; we can see that in the segment seven there is only mild cross-dependence, but in segment eight the readings at Wells Road, St. Paul’s, and Fishponds Road become strongly dependent on the other series.

 

Estimated parameters for segments seven and eight, with lagged variables by row

ACF of entire series at site 215 over three time scales

Scientists have generally worked under the belief that these concentration series have long-memory behaviour, meaning values long in the past have influence on today’s values, and that this explains why the autocorrelation function (ACF) decays slowly, as seen above. Perhaps the most interesting conclusion we can draw from this analysis is that that structural changes explain this slow decay – the image below displays much shorter range dependence for one such stationary segment.

ACF at each site on segment one

 

Conclusion

After all our work, we have a simple, interpretable model for NOx levels. In reality, physical processes often depend on each other in a non-linear fashion, and according to the geographical distances between where they are measured; accounting for this might provide better predictions, or tell a slightly different story.  Moreover, there should be interactions with the other pollutants present in the air. Could we combine these for a large-scale, comprehensive air quality model? Perhaps the most important question to ask, however, is how this analysis can be used to benefit policy makers. If we could combine this with a causal model, we might we be able to identify policy outcomes, or to draw comparisons with other regions.

Student perspectives: Stein’s paradox in statistics

A post by Alexander Modell, PhD student on the Compass programme.

Stein’s paradox in statistics

In 1922, Sir R.A. Fisher laid the foundations of classical statistics with an idea he named maximum likelihood estimation. His principle, for determining the parameters of a statistical model, simply states that given some data, you should choose the parameters which maximise the probability of observing the data that you actually observed. It is based on a solid philosophical foundation which Fisher termed “logic of inductive reasoning” and provides intuitive estimators for all sorts of statistical problems. As a result, it is frequently cited as one of the most influential pieces of applied mathematics of the twentieth century. Eminent statistician Bradley Efron even goes as far as to write

“If Fisher had lived in the era of “apps”, maximum likelihood estimation would have made him a billionaire.” [1]

It came as a major shock to the statistical world when, in 1961, James and Stein exposed a paradox that had the potential to undermine what had become the backbone of classical statistics [2].

It goes something like this.

Suppose I choose a real number on a number line, and instead of telling you that number directly, I add some noise, that is, I perturb it a little and give you that perturbed number instead. We’ll assume that the noise I add is normally distributed and, to keep things simple, has variance one. I ask you to guess what my original number was – what do you do? With no other information to go on, most people would guess the number that I gave them, and they’d be right to do so. By a commonly accepted notion of what “good” is (we’ll describe this a little later on) – this is a “good” guess! Notably, this is also the maximum likelihood estimator.

Suppose now that I have two real numbers and, again, I independently add some noise to each and tell you those numbers. What is your guess of my original numbers? For the same reasons as before, you choose the numbers I gave you. Again, this is a good guess!

Now suppose I have three numbers. As usual, I independently add some noise to each one and tell you those numbers. Of course, you guess the numbers I gave you, right? Well, this no longer the best thing to do! If we imagine those three numbers as a point in three-dimensional space, you can actually do better by arbitrarily choosing some other point, let’s call it B, and shifting your estimate slightly from the point I gave you towards B. This is Stein’s paradox. At first glance, it seems rather absurd. Even after taking some time to take in its simple statement and proof, it’s almost impossible to see how it can possibly be true.

To keep things simple, from now on we will assume that B is the origin, that is that each dimension of B is zero, although keep in mind that all of this holds for any arbitrary point. Stated more formally, the estimator proposed by James and Stein is

\hat{\mu}_i^{\text{(JS)}} = \left( 1 - \frac{(p-2)}{x_1^2+\cdots + x_p^2} \right) x_i.

where p is the number of numbers you are estimating, and x_1,\ldots,x_p are the noisy versions that I give you. Since the term in brackets is always less than one, their estimator shrinks each of the dimensions of the observation towards zero. For this reason, it is known as a “shrinkage” estimator. There are two central surprises here. Firstly, it seems totally paradoxical to estimate anything other than the values I gave you. With no other information, why on earth would you estimate anything different? Secondly, their estimate of one value depends on the observations of all of the other values, despite the fact that they’re totally independent!

To most, this is seemingly ludicrous, and to make this point, in his paper on the phenomenon [3], Richard Samworth presents an unusual example. Suppose we want to estimate the proportion of the US electorate who will vote for Barack Obama, the proportion of babies born in China that are girls and the proportion of Britons with light-coloured eyes, then our James-Stein estimate of the proportion of Democratic voters will depend on our hospital and eye colour data!

Before we discuss what we mean when we say that the James-Stein estimator is “better” than the maximum likelihood estimator, let’s take a step back and get some visual intuition as to why this might be true.

Suppose I have a circle on an infinite grid and I task you with guessing the position of the centre of that circle. Instead of just telling you the location of the centre, I draw a point from the circle at random and tell you that position. Let’s call this position A and we’ll call the true, unknown centre of the circle C. What do you propose as your guess? Intuition tells you to guess A, but Stein’s paradox would suggest choosing some other point B, and shifting your guess towards it.

Figure showing the proportion of a circle which could get closer to the centre by moving it towards the originLet’s suppose the circle has radius one and its true centre is at x=0,y=2. To keep things simple, suppose I set the point B to be the origin (that is x=0,y=0). Now, if I randomly draw a point from this circle, what proportion of the time will my guess actually be better if I shrink it slightly towards the point B. For this illustration, we’ll just consider shrinking towards B a very small amount, so we can rephrase this question as this: What proportion of the circle could get closer to the centre if it were shrunk towards B a little?

The answer to that question is the region marked “2” on the figure. A bit of geometry tells us that that’s about 61% of the circle, a little over half. So estimating a point slighter closer to B than the point we drew has the potential to improve our estimate more times than it hinders it! In fact, this holds true regardless of the position of B.

Now let’s consider this problem in three dimensions. We now have a sphere rather than a circle but everything else remains the same. In three dimensions, the region marked “2” covers just over 79% of the sphere, so about four times out of five, our shrinkage estimator does better than estimating the point A. If you’ve just tried to imagine a four-dimensional sphere, your head probably hurts a little but you can still do this maths. Region “2” now covers 87% of the sphere. In ten dimensions, it covers about 98% of the sphere and in one hundred dimensions it covers 99.99999% of it. That is to say, that only one in ten millions times, our estimate couldn’t be improved by shrinking it towards B.

Hopefully, you’re starting it see how powerful this idea can be, and how much more powerful it becomes as we move into higher dimensions. Despite this, the more you think about it, the more something seems amiss – yet it’s not.

Statisticians typically determine how “good” an estimator is through what is called a loss function. A loss function can be viewed as a penalty that increases the further your estimate is from the underlying truth. Examples are the squared error loss \ell_2(\hat{\mu};\mu) = \sum_i(\hat{\mu}_i - \mu_i)^2, which is by far the most common choice, and the absolute error loss \ell_1(\hat{\mu};\mu)\ = \sum_i |\hat{\mu}_i - \mu_i| where \hat{\mu} is the estimate and \mu is the truth. Since the loss function depends on the data that was observed, it is not suitable for comparing estimators, so instead the risk of the estimator is used, which is defined as the loss we would expect to see if we drew a random dataset from the underlying distribution.

The astonishing result that James and Stein proved, was that, under the squared error loss, their “shrinkage” estimator has a lower risk than the maximum likelihood estimator, regardless of the underlying truth. This came as a huge surprise to the statistics community at the time, which was firmly rooted in Fisher’s principle of maximum likelihood estimation.

It is natural to ask whether this is a quirk of the normal distribution and the squared error loss, but in fact, similar phenomena have been shown for a wide range of distributions and loss functions.

It would be easy to shrug this result off as merely a mathematical curiosity – many contemporaries of Stein did – yet in the era of big data, the ideas underpinning it have proved crucial to modern statistical methodology. Modern datasets regularly contain tens of thousands, if not millions of dimensions and in these settings, classical statistical ideas break down. Many modern machine learning algorithms, such as ridge and Lasso regression, are underpinned by these ideas of shrinkage.

References

[1]: Efron, B., & Hastie, T. (2016). Computer age statistical inference (Vol. 5). Cambridge University Press.

[2]: James, W. and Stein, C.M. (1961) Estimation with Quadratic Loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 361-379, University California Press, Berkeley.

[3]: Samworth, R. J., & Cambridge, S. (2012). Stein’s paradox. eureka, 62, 38-41.

Student Perspectives: LV Datathon – Insurance Claim Prediction

A post by Doug Corbin, PhD student on the Compass programme.

Introduction

In a recent bout of friendly competition, students from the Compass and Interactive AI CDT’s were divided into eight teams to take part in a two week Datathon, hosted by insurance provider LV. A Datathon is a short, intensive competition, posing a data-driven challenge to the teams. The challenge was to construct the best predictive model for (the size of) insurance claims, using an anonymised, artificial data set generously provided by LV. Each team’s solution was given three important criteria, on which their solutions would be judged:

  • Accuracy – How well the solution performs at predicting insurance claims.
  • Explainability – The ability to understand and explain how the solution calculates its predictions; It is important to be able to explain to a customers how their quote has been calculated.
  • Creativity – The solution’s incorporation of new and unique ideas.

Students were given the opportunity to put their experience in Data Science and Artificial Intelligence to the test on something resembling real life data, forming cross-CDT relationships in the process.

Data and Modelling

Before training a machine learning model, the data must first be processed into a numerical format. To achieve this, most teams transformed categorical features into a series of 0’s and 1’s (representing the value of the category), using a well known process called one-hot encoding. Others recognised that certain features had a natural order to them, and opted to map them to integers corresponding to their ordered position.

A common consideration amongst all the teams’ analysis was feature importance. Out of the many methods/algorithms which were used to evaluate the relative importance of the features, notable mentions are Decision Trees, LASSO optimisation, Permutation Importance and SHAP Values. The specific details of these methods are beyond the scope of this blog post, but they all share a common goal, to rank features according to how important they are in predicting claim amounts. In many of the solutions, feature importance was used to simplify the models by excluding features with little to no predictive power. For others, it was used as a post analysis step to increase explainability i.e. to show which features where most important for a particular claim prediction. As a part of the feature selection process, all teams considered the ethical implications of the data, with many choosing to exclude certain features to mitigate social bias.

Interestingly, almost all teams incorporated some form of Gradient Boosted Decision Trees (GBDT) into their solution, either for feature selection or regression. This involves constructing multiple decision trees, which are aggregated to give the final prediction. A decision tree can be thought of as a sequence of binary questions about the features (e.g. Is the insured vehicle a sports car? Is the car a write off?), which lead to a (constant) prediction depending on the the answers. In the case of GBDT, decision trees are constructed sequentially, each new tree attempting to capture structure in the data which has been overlooked by its predecessors. The final estimate is a weighted sum of the trees, where the weights are optimised using (the gradient of) a specified loss function e.g. Mean-Squared Error (MSE),

MSE = \frac{1}{N} \sum_{n = 1}^N (y_n - \hat{y}_n)^2.

Many of the teams trialled multiple regression models, before ultimately settling on a tree-based model. However, it is well-known that tree-based models are prone to overfitting the training data. Indeed, many of the teams were surprised to see such significant difference between the training/testing Mean Absolute Error (MAE),

MAE = \frac{1}{N} \sum_{n = 1}^N |y_n - \hat{y}_n|.

Results

After two weeks of hard-work, the students came forward to present their solutions to a judging panel formed of LV representatives and CDT directors. The success of each solution was measured via the MAE of their predictions on the testing data set. Anxious to find out the results, the following winners were announced.

Accuracy Winners

Pre-processing: Categorical features one-hot encoded or mapped to integers where appropriate.

Regression Model: Gradient Boosted Decision Trees.

Testing MAE: 69.77

The winning team (in accuracy) was able to dramatically reduce their testing MAE through their choice of loss function. Loss functions quantify how good/bad a regression model is performing during the training process, and it is used to optimise the linear combination of decision trees. While most teams used the popular loss function, Mean-Squared Error, the winning team instead used Least Absolute Deviationswhich is equivalent to optimising for the MAE while training the model.

Explainability (Joint) Winners

After much deliberation amongst the judging panel, two teams were awarded joint first place in the explainability category!

Team 1: 

Pre-processing: Categorical features one-hot encoded or mapped to integers where appropriate. Features centred and scaled to have mean 0 and standard deviation 1, then selected using Gradient Boosted Decision Trees.

Regression Model: K-Nearest-Neighbours Regression

Testing MAE: 75.25

This team used Gradient Boosted Decision Trees for feature selection, combined with K-Nearest-Neighbours (KNN) Regression to model the claim amounts. KNN regression is simple in nature; given a new claim to be predicted, the K “most similar” claims in the training set are averaged (weighted according to similarity) to produce the final prediction. It is thus explainable in the sense that for every prediction you can see exactly which neighbours contributed, and what similarities they shared. The judging panel noted that, from a consumer’s perspective, they may not be satisfied with their insurance quote being based on just K neighbours.

Team 2:

Pre-processing: All categorical features one-hot encoded.

Regression Model: Gradient Boosted Decision Trees. SHAP values used for post-analysis explainability.

Testing MAE: 80.3.

The judging panel was impressed by this team’s decision to impose monotonicity in the claim predictions with respect to the numerical features. This asserts that, for monotonic features, the claim prediction must move in a constant direction (increasing or decreasing) if the numerical feature is moving in a constant direction. For example, a customer’s policy excess is the amount they will have to pay towards a claim made on their insurance. It stands to reason that increasing the policy excess (while other features remain constant) should not increase their insurance quote. If this constraint is satisfied, we say that the insurance quote is monotonic decreasing with respect to the policy excess. Further, SHAP values were used to explain the importance/effect of each feature on the model.

Creativity Winners

Pre-processing: Categorical features one-hot encoded or mapped to integers where appropriate. New feature engineered from Vehicle Size and Vehicle Class. Features selected using Permutation Importance.

Regression Model: Gradient Boosted Decision Trees. Presented post-analysis mediation/moderation study of the features.

Testing MAE: 76.313.

The winning team for creativity presented unique and intriguing methods for understanding and manipulating the data. This team noticed that the features, Vehicle Size and Vehicle Class, are intrinsically related e.g. They investigated whether a large vehicle would likely yield a higher claim if it is also of luxury vehicle class. To represent this relationship, they engineered a new feature by taking a multiplicative combination of the two initial features.

As an extension of their solution, they presented an investigation of the causal relationship between the different features. Several hypothesis tests were performed, testing whether the relationship between certain features and claim amounts is moderated or mediated by an alternative feature in the data set.

  • Mediating relationships: If a feature is mediated by an alternative feature in the data set, its relationship with the claim amounts can be well explained by the alternative (potentially indicating it can be removed from the model).
  • Moderating relationships: If a feature is moderated by an alternative feature in the data set, the strength and/or direction of the relationship with the claim amounts is impacted by the alternative.

Final Thoughts

All the teams showed a great understanding of the problem and identified promising solutions. The competitive atmosphere of the LV Datathon created a notable buzz amongst the students, who were eager to present and compare their findings. As evidenced by every team’s solution, the methodological conclusion is clear: When it comes to insurance claim prediction, tree-based models are unbeaten!

Student perspectives: Wessex Water Industry Focus Lab

A post by Michael Whitehouse, PhD student on the Compass programme.

Introduction

September saw the first of an exciting new series of Compass industry focus labs; with this came the chance to make use of the extensive skill sets acquired throughout the course and an opportunity to provide solutions to pressing issues of modern industry. The partner for the first focus lab, Wessex Water, posed the following question: given time series data on water flow levels in pipes, can we detect if new leaks have occurred? Given the inherent value of clean water available at the point of use and the detriments of leaking this vital resource, the challenge of ensuring an efficient system of delivery is of great importance. Hence, finding an answer to this question has the potential to provide huge economic, political, and environmental benefits for a large base of service users.

Data and Modelling:

The dataset provided by Wessex Water consisted of water flow data spanning across around 760 pipes. After this data was cleaned and processed some useful series, such as minimum nightly and average daily flow (MNF and ADF resp.), were extracted. Preliminary analysis carried out by our collaborators at Wessex Water concluded that certain types of changes in the structure of water flow data provide good indications that a leak has occurred. From this one can postulate that detecting a leak amounts to detecting these structural changes in this data. Using this principle, we began to build a framework to build solutions: detect the change; detect a new leak. Change point detection is a well-researched discipline that provides us with efficient methods for detecting statistically significant changes in the distribution of a time series and hence a toolbox with which to tackle the problem. Indeed, we at Compass have our very own active member of the change point detection research community in the shape of Dom Owens. The preliminary analysis gave that there are three types of structural change in water flow series that indicate a leak: a change in the mean of the MNF, a change in trend of the MNF, and a change in the variance of the difference between the MNF and ADF. In order to detect these changes with an algorithm we would need to transform the given series so that the original change in distribution corresponded to a change in the mean of the transformed series. These transforms included calculating generalised additive model (GAM) residuals and analysing their distribution. An example of such a GAM is given by:

\mathbb{E}[\text{flow}_t] = \beta_0 \sum_{i=1}^m f_i(x_i).

Where the x i ’s are features we want to use to predict the flow, such as the time of day or current season. The principle behind this analysis is that any change in the residual distribution corresponds to a violation of the assumption that residuals are independently, identically distributed and hence, in turn, corresponds to a deviation from the original structure we fit our GAM to.

Figure 1: GAM residual plot. Red lines correspond to detected changes in distribution, green lines indicate a repair took place.

A Change Point Detection Algorithm:

In order to detect changes in real time we would need an online change point detection algorithm, after evaluating the existing literature we elected to follow the mean change detection procedure described in [Wang and Samworth, 2016]. The user-end procedure is as follows:

  1. Calculate mean estimate \hat{\mu} on some data we assume is stationary.
  2. Feed a new observation into the algorithm. Calculate test statistics based on new data.
  3. Repeat (2) until any test statistics exceed a threshold at which point we conclude a mean change has been detected. Return to (1).

Due to our 2 week time restraint we chose to restrict ourselves to finding change points corresponding to a mean change, just one of the 3 changes we know are indicative of a leak. As per the fundamental principles of decision theory, we would like to tune and evaluate our algorithm by minimising some loss function which depends on some ‘training’ data. That is, we would like to look at some past period of time and make predictions of when leaks happened given the flow data across the same period, then we evaluate how accurate these predictions were and adjust or asses the model accordingly. However, to do this we would need to know when and where leaks actually occurred across the time period of the data, something we did not have access to. Without ‘labels’ indicating that a leak has occurred, any predictions from the model were essentially meaningless, so we sought to find a proxy. The one potentially useful dataset we did have access to was that of leak repairs. It is clear that a leak must have occurred if a repair has occurred, but for various reasons this proxy does not provide an exhaustive account of all leaks. Furthermore, we do not know which repairs correspond to leaks identified by the particular distributional change in flow data we considered. This, in turn, means that all measures of model performance must come with the caveat that they are contingent on incomplete data. If when conducting research we find out results are limited it is our duty as statisticians to report when this is the case – it is not our job to sugar coat or manipulate our findings, but to report them with the limitations and uncertainty that inextricably come alongside. Results without uncertainty are as meaningless as no results at all. This being said, all indications pointed towards the method being effective in detecting water flow data mean change points which correspond to leak repairs, giving a positive result to feedback to our friends at Wessex Water.

Final Conclusions:

Communicating statistical concepts and results to an audience of varied areas and levels of expertise is important now more than ever. The continually strengthening relationships between Compass and its industrial partners are providing students with the opportunity to gain experience in doing exactly this. The focus lab concluded with a presentation on our findings to the Wessex Water operations team,  during which we reported the procedures and results. The technical results were well supported by the demonstration of an R shiny dashboard app, which provided an intuitive interface to view the output of the developed algorithm. Of course, there is more work to be done. Expanding the algorithm to account for all 3 types of distributional change is the obvious next step. Furthermore, fitting a GAM to data for 760 pipes is not very efficient. Additional investigations into finding ways to ‘cluster’ groups of pipes together according to some notion of similarity is a natural avenue for future work in order to reduce the number of GAMS we need to fit.This experience enabled students to apply skills in statistical modelling, algorithm development, and software development to a salient problem faced by an industry partner and marked a successful start to the Compass industry focus lab series.

Student perspectives: Three Days in the life of a Silicon Gorge Start-Up

A post by Mauro Camara Escudero, PhD student on the Compass programme.

Last December the first Compass cohort partook a 3-day entrepreneurship training with SpinUp Science. Keep reading and you might just find out if the Silicon Gorge life is for you!

The Ambitious Project of SpinUp Science

SpinUp Science’s goal is to help PhD students like us develop an entrepreneurial skill-set that will come in handy if we decide to either commercialize a product, launch a start-up, or choose a consulting career.

I can already hear some of you murmur “Sure, this might be helpful for someone doing a much more applied PhD but my work is theoretical. How is that ever going to help me?”. I get that, I used to believe the same. However, partly thanks to this training, I changed my mind and realized just how valuable these skills are independently of whether you decide to stay in Academia or find a job at an established company.

Anyways, I am getting ahead of myself. Let me first guide you through what the training looked like and then we will come back to this!

Day 1 – Meeting the Client

The day started with a presentation that, on paper, promised to be yet another one of those endless and boring talks that make you reach for the Stop Video button and take a nap. The vague title “Understanding the Opportunity” surely did not help either. Instead, we were thrown right into action!

Ric and Crescent, two consultants at SpinUp Science, introduced us to their online platform where we would be spending most of our time in the next few days. Our main task for the first half-hour was to read about the start-up’s goals and then write down a series of questions to ask the founders in order to get a full picture.

Before we knew it, it was time to get ready for the client meeting and split tasks. I volunteered as Client Handler, meaning I was going to coordinate our interaction with the founders. The rest of Compass split into groups focusing on different areas: some were going to ask questions about competitors, others about the start-up product, and so on.

As we waited in the ZOOM call, I kept wondering why on earth I volunteered for the role and my initial excitement was quickly turning into despair. We had never met the founders before, let alone had any experience consulting or working for a start-up.

Once the founders joined us, and after a wobbly start, it became clear that the hard part would not be avoiding awkward silences or struggling to get information. The real challenge was being able fit all of our questions in this one-hour meeting. One thing was clear: clients love to talk about their issues and to digress.

After the meeting, we had a brief chat and wrote down our findings and thoughts on the online platform. I wish I could say we knew what we were doing, but in reality it was a mix of extreme winging and following the advice of Ric and Crescent.

Last on the agenda, was a short presentation where we learned how to go about studying the market-fit for a product, judge its competitors and potential clients, and overall how to evaluate the success of a start-up idea. That was it for the day, but the following morning we would put into practice everything we had learned up to that point.

Day 2 – Putting our Stalking Skills to good use

The start-up that we were consulting for provides data analysis software for power plants and was keen to expand in a new geographical area. Our goal for the day was therefore to:

  • understand the need for such a product in the energy market

  • research what options are available for the components of their product

  • find potential competitors and assess their offering

  • find potential clients and assess whether they already had a similar solution implemented

  • study the market in the new geographical area

This was done with a mix of good-old Google searches and cold-calling. It was a very interesting process as in the morning we were even struggling to understand what the start-up was offering, while by late afternoon we had a fairly in-depth knowledge of all the points above and we had gathered enough information to formulate more sensible questions and to assess the feasibility of the start-up’s product. One of the things I found most striking about this supervised hands-on training is that as time went on I could clearly see how I was able to filter out useless information and go to the core of what I was researching.

To aid us in our analyses, we received training on various techniques to assess competitors, clients and the financial prospect of a start-up. In addition, we also learned about why the UK is such a good place to launch a start-up, what kind of funding is available and how to look for investors and angels.

Exhausted by a day of intense researching, we knew the most demanding moments were yet to come.

Day 3 – Reporting to the Client

The final day was all geared towards preparing for our final client meeting. Ric and Crescent taught us how to use their online platform to perform PESTEL and SWOT analyses efficiently based on the insights that we gathered the day before. It was very powerful seeing a detailed report coming to life using inputs from all of our researches.

With the report in hand, several hours of training under our belt, and a clearer picture in our head, we joined the call and each one of us presented a different section of the report, while Andrea was orchestrating the interaction. Overall, the founders seemed quite impressed and admitted that had not heard of many of the competitors we had found. They were pleased by our in-depth research and, I am sure, found it very insightful.

Lessons Learned

So, was it useful?

I believe that this training gave us a glimpse of how to go about picking up a totally new area of knowledge and quickly becoming an expert on it. The time constraint allowed us to refine the way in which we filter out useless information, to get to the core of what we are trying to learn about. We also worked together as a team towards a single goal and we formulated our opinion on the start-up. Finally, we had two invaluable opportunities to present in a real-world setting and to handle diplomatically the relationship with the client.

In the end, isn’t research all about being able to pickup new knowledge quickly, filter out useless papers, working together with other researchers to develop a method and present such results to an audience?

Find out more about Mauro Camara Escudero and his work on his profile page.

Student perspectives: The Elo Rating System – From Chess to Education

A post by Andrea Becsek, PhD student on the Compass programme.

If you have recently also binge-watched the Queen’s Gambit chances are you have heard of the Elo Rating System. There are actually many games out there that require some way to rank players or even teams. However, the applications of the Elo Rating System reach further than you think.

History and Applications

The Elo Rating System 1 was first suggested as a way to rank chess players, however, it can be used in any competitive two-player game that requires a ranking of its players. The system was first adopted by the World Chess Federation in 1970, and there have been various adjustments to it since, resulting in different implementations by each organisation.

For any soccer-lovers out there, the FIFA world rankings are also based on the Elo System, but if you happen to be into a similar sport, worry not, Elo has you covered. And the list of applications goes on and on: Backgammon, Scrabble, Go, Pokemon, and apparently even Tinder used it at some point.

Fun fact: The formulas used by the Elo Rating make a famous appearance in the Social Network, a movie about the creation of Facebook. Whether this was the actual algorithm used for FaceMash, the first version of Facebook, is however unclear.

All this sounds pretty cool, but how does it actually work?

How it works

We want a way to rank players and update their ranking after each game. Let’s start by assuming that we have the ranking for the two players about to play: \theta_i for player i and \theta_j for player j. Then we can compute the probability of player i winning against player j using the logistic function:

P(Y_{ij}=1)=\frac{1}{1+\exp\{-(\theta_i-\theta_j)\}}.

Given what we know about the logistic function, it’s easy to notice that the smaller the difference between the players, the less certain the outcome as the probability of winning will be close to 0.5.

Once the outcome of the game is known, we can update both players’ abilities

\theta_{i}:=\theta_{i}+K(Y_{ij}-P(Y_{ij}=1))

\theta_{j}:=\theta_{j}+K(P(Y_{ij}=1)-Y_{ij}).

The K factor controls the influence of a player’s performance on their previous ranking. For players with high rankings, a smaller K is used because we expect their abilities to be somewhat stable and hence their ranking shouldn’t be too heavily influenced by every game. On the other hand, players with low ability can learn and improve quite quickly, and therefore their rating should be able to fluctuate more so they have a larger K number.

The term in the brackets represents how different the actual outcome is from the expected outcome of the game. If a player is expected to win but doesn’t, their ranking will decrease, and vice versa. The larger the difference, the more their rating will change. For example, if a weaker player is highly unlikely to win, but they do, their ranking will be boosted quite a bit because it was a hard battle for them. On the other hand, if a strong player is really likely to win because they are playing against a weak player, their increase in score will be small as it was an easy win for them.

Elo Rating and Education

As previously mentioned, the Elo Rating System has been used in a wide range of fields and, as it turns out, that includes education, more specifically, adaptive educational systems 2. Adaptive educational systems are concerned with automatically selecting adequate material for a student depending on their previous performance.

Note that a system can be adaptive at different levels of granularity. Some systems might adapt the homework from week to week by generating it based on the student’s current ability and update their ability once the homework has been completed. Whereas other systems are able to update the student ability after every single question. As you can imagine, using the second system requires a fairly fast, online algorithm. And this is where the Elo Rating comes in.

For an adaptive system to work, we need two key components: student abilities and question difficulties. To apply the Elo Rating to this context, we treat a student’s interaction with a question as a game where the student’s ranking represents their ability and the question’s ranking represents its difficulty. We can then predict whether a student of ability \theta_i will answer a question of difficulty d_j correctly using

P(\text{correct}_{ij}=1)=\frac{1}{1+\exp\{-(\theta_i-d_j)\}}.

and the ability and difficulty can be updated using

\theta_{i}:=\theta_{i}+K(\text{correct}_{ij}-P(\text{correct}_{ij}=1))

d_{j}:=d_{j}+K(P(\text{correct}_{ij}=1)-\text{correct}_{ij}).

So even if you only have 10 minutes to create an adaptive educational system you can easily implement this algorithm. Set all abilities and question difficulties to 0, let students answer your questions, and wait for the magic to happen. If you do have some prior knowledge about the difficulty of the items you could of course incorporate that into the initial values.

One important thing to note is that one should be careful with ranking students based on their abilities as this could result in various ethical issues. The main purpose of obtaining their abilities is to track their progress and match them with questions that are at the right level for them, easy enough to stay motivated, but hard enough to feel challenged.

Conclusion

So is Elo the best option for an adaptive system? It depends. It is fast, enables on the fly updates, it’s easy to implement, and in some contexts, it even has a similar performance to more complex models. However, there are usually many other factors that can be relevant to predicting student performance, such as the time spent on a question or the number of hints they use. This additional data can be incorporated into more complex models, probably resulting in better predictions and offering much more insight. At the end of the day, there is always a trade-off, so depending on the context it’s up to you to decide whether the Elo Rating System is the way to go.

Find out more about Andrea Becsek and her work on her profile page.

  1. Elo, A.E., 1978. The rating of chessplayers, past and present. Arco Pub.
  2. Pelánek, R., 2016. Applications of the Elo rating system in adaptive educational systems. Computers & Education, 98, pp.169-179.
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