An Introduction to (Dynamic) Nested Sampling and Model Selection

I will present a brief introduction to Nested Sampling, a complementary framework to Markov Chain Monte Carlo approaches that is designed to estimate marginal likelihoods (i.e. Bayesian evidences) and posterior distributions. This will include some discussion on the philosophical distinctions and motivations of Nested Sampling, a few ways of understanding why/how it works, some of its pros and cons, and more recent extensions such as Dynamic Nested Sampling. If time/interest permits, I can either (a) highlight how this can work in practice using the public Python package dynesty​ or (b) discuss the more general problem of model selection and why Bayesian evidences may (or may not) be helpful.

Zoom Link: https://pitp.zoom.us/j/95990705337?pwd=VzB4cjhzSDhoM0RCYTNwZHUzUVlzdz09