# Tag: statistics

## Compass Conference 2022

Our first Compass Conference was held on Tuesday 13^{th} September 2022, hosted in the newly refurbished Fry Building, home to the School of Mathematics.

The conference was a celebratory showcase of the achievements of our students, supervisory teams, and collaborations with industrial partners. Attendees were invited from a diverse range of organisations outside of academia as well as academic colleagues from across the University of Bristol.

## Programme

### Lightning talks: 3 min presentations from Compass PhD students

- Mauro Camara Escudero: Approximate Manifold Sampling
- Doug Corbin: Partitioned Polynomial Thompson Sampling for Contextual Multi-Armed Bandits.
- Dom Owens: FNETS: An R Package for Network Analysis and Forecasting of High-Dimensional Time Series with Factor-Adjusted Vector Autoregressive Models
- Jake Spiteri: A non-parametric method for state-space models
- Daniel Williams: Kernelised Stein Discrepancies for Truncated Probability Density Estimation
- Conor Crilly: Efficient Emulation of a Radionuclide Transport Model
- Annie Gray: Discovering latent topology and geometry in data: a law of large dimension
- Conor Newton: Mutli-Agent Multi-Armed Bandits
- Jack Simons: Variational Likelihood-Free Gradient Descent
- Anthony Stephenson: Provably Reliable Large-Scale Sampling from Gaussian Processes
- Dan Ward: Robust Neural Posterior Estimation
- Shannon Williams: Sampling Schemes for Volcanic Ash Dispersion Hazard Assessment
- Dominic Broadbent: Bayesian Coresets Versus the Laplace Approximation
- Emerald Dilworth: Using Web Data and Network Embedding to Detect Spatial Relationships
- Ettore Fincato: Markov state modelling and Gibbs sampling
- Josh Givens: DRE and NP Classification with Missing Data
- Ben Griffiths: Faster Model Fitting for Quantile Additive Models
- Tennessee Hickling: Flexible Tails for Normalising Flows
- Daniel Milner: When Does Market Access Improve Smallholder Nutrition? A Multilevel Analysis
- Edward Milsom: Deep Kernel Machines

### Research talks

- Ed Davis –
*Universal Dynamic Network Embedding – How to Comprehend Changes in 20,000 Dimensions* - Ettore Fincato –
*Spectral analysis of the Gibbs sampler with the concept of conductance* - Alexander Modell –
*Network community detection under degree heterogeneity: spectral clustering with the random walk Laplacian* - Hannah Sansford –
*Implications of sparsity and high triangle density for graph representation learning* - Michael Whitehouse –
*Consistent and fast inference in compartmental models of epidemics via Poisson Approximate Likelihoods* - Alessio Zakaria –
*Your Favourite Optimizer may not Converge: Click here to see more*

### Special guest lecture

John Burn-Murdoch, Chief Data Reporter at the Financial Times.

Making charts that make an impact: An exploration of what makes data visualisation effective as a means of communication, drawing on the latest scientific research, plus John’s experiences from visualising the pandemic.

## Attendees

Attendees included academics associated with Compass from across the University of Bristol. Our external attendees were invited from the following partner organisations.

Adarga

Advai

Alan Turing Institute

Allianz Personal

AWE

British Telecom

CheckRisk LLP

COVID-19 Actuaries Response Group

Financial Times

GSK

Improbable

Infinitesima

International Livestock Research Institute

LV= General Insurance

Met Office

NVIDIA

TGE Data Science

Trilateral Research

UK Health Security Agency

## Access to Data Science 2022

## Student Perspectives: Contemporary Ideas in Statistical Philosophy

A post by Alessio Zakaria, PhD student on the Compass programme.

## Introduction

Probability theory is a branch of mathematics centred around the abstract manipulation and quantification of uncertainty and variability. It forms a basic unit of the theory and practice of statistics, enabling us to tame the complex nature of observable phenomena into meaningful information. It is through this reliance that the debate over the *true* (or more correct) underlying nature of probability theory has profound effects on how statisticians do their work. The current opposing sides of the debate in question are the *Frequentists* and the *Bayesians*. Frequentists believe that probability is intrinsically linked to the numeric regularity with which events occur, i.e. their frequency. Bayesians, however, believe that probability is an expression of someones degree of belief or confidence in a certain claim. In everyday parlance we use both of these concepts interchangeably: I estimate one in five of people have Covid; I was 50% confident that the football was coming home. It should be noted that the latter of the two is not a repeatable event per se. We cannot roll back time to check what the repeatable sequence would result in.