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

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• Will Handley - Kavli
• Thursday 14 November 2019, 13:00-14:30
• Kavli Large Meeting Room, Kavli Building.

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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