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Nested Sampling: an efficient and robust Bayesian inference tool for Machine Learning and Data Science

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If you have a question about this talk, please contact James Fergusson .

Nested sampling is an MCMC technique for integrating and exploring probability distributions. It has become widely adopted in the field of cosmology as a powerful tool for computing Bayesian evidences and sampling challenging a-priori unknown parameter spaces.

In this talk, I will give an introduction to the principles of Bayesian model comparison and parameter estimation, an explanation of the theory of nested sampling, a survey of the current state-of-the art (MultiNest, PolyChord, DNest and Dynesty) and the future of the field. I will illustrate with applications in CMB and 21cm Cosmology, Bayesian Sparse Reconstruction and Bayesian Neural Networks.

This talk is part of the Data Intensive Science Seminar Series series.

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