A post by Sam Stockman, PhD student on the Compass programme.
Throughout my PhD I aim to bridge a gap between advances made in the machine learning community and the age-old problem of earthquake forecasting. In this inter-departmental work with Max Werner from the school of Earth Sciences and Dan Lawson from the school of Maths, I hope to create more powerful, efficient and robust models for forecasting, that can make earthquake prone areas safer for their inhabitants.
For years Seismologists have sought to model the structure and dynamics of the earth in order to make predictions about earthquakes. They have mapped out the structure of fault lines and conducted experiments in the lab where they submit rock to great amounts of force in order to simulate plate tectonics on a small scale. Yet when trying to forecast earthquakes on a short time scale (that’s hours and days, not tens of years), these models based on the knowledge of the underlying physics are regularly outperformed by models that are statistically motivated. In statistical seismology we seek to make predictions through looking at distributions of the times, locations and magnitudes of earthquakes and use them to forecast the future.
A post by Jack Simons, PhD student on the Compass programme.
I began my PhD with my supervisors, Dr Song Liu and Professor Mark Beaumont with the intention of combining their respective fields of research; Density Ratio Estimation (DRE), and Simulation Based Inference (SBI):
- DRE is a rapidly growing paradigm in machine learning which (broadly) provides efficient methods of comparing densities without the need to compute each density individually. For a comprehensive yet accessible overview of DRE in Machine Learning see .
- SBI is a group of methods which seek to solve Bayesian inference problems when the likelihood function is intractable. If you wish for a concise overview of the current work, as well as motivation then I recommend .
Last year we released a paper, Variational Likelihood-Free Gradient Descent  which combined these fields. This blog post seeks to condense, and make more accessible, the contents of the paper.
Motivation: Likelihood-Free Inference
Let’s begin by introducing likelihood-free inference. We wish to do inference on the posterior distribution of parameters for a specific observation , i.e. we wish to infer which can be decomposed via Bayes’ rule as
The likelihood-free setting is that, additional to the usual intractability of the normalising constant in the denominator, the likelihood, , is also intractable. In lieu of this, we require an implicit likelihood which describes the relation between data and parameters in the form of a forward model/simulator (hence simulation based inference!). (more…)
A post by Alessio Zakaria, PhD student on the Compass programme.
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.