Nested Sampling: Past, Present, and Future (Dr. Joshua Speagle)

Dr. Speagle will provide an overview of 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. He will start by discussing some of the technical details and history behind the approach, focusing on its motivation, formulation, and execution (the Past). He will then explore some of the challenges that are involved with practical implementations along with associated properties that have made Nested Sampling quite popular within the physical sciences (the Present). He will finally close by discussing ongoing work designed to address some of these issues along with additional longer-term (and potentially existential) challenges for the method (the Future). If time permits, we may also discuss the potential benefits of incorporating Nested Sampling into applied Bayesian education materials.