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Performing Bayesian Model Selection with Nested Sampling

Lecturer: Farhan Feroz (Cambridge)

Lecture abstract.

Astrophysics and cosmology have increasingly become data driven with the availability of large amount of high quality data from missions like WMAP, Planck and LHC. This has resulted in Bayesian inference methods being widely used to analyse observations, but they can be extremely computationally demanding. I will discuss the challenges involved in performing Bayesian model selection and discuss how it is done in practice. In particular, I will describe the MultiNest algorithm, which is based on a Monte Carlo technique called Nested Sampling. MultiNest has been applied successfully to numerous challenging problems in cosmology and astroparticle physics due to its capability of efficiently exploring multi-modal parameter spaces. MultiNest can also calculate the Bayesian evidence and therefore provides means to carry out Bayesian model selection. I will also review some of the recent applications of MultiNest in astrophysics.

A journey through Gaussian random fields with a view towards Bayesian optimization.
Basic MCMC: implementing the Metropolis algorithm.
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