CS Katha Barta | Dr. Dootika Vats, IIT Kanpur
Speaker : Dr. Dootika Vats, IIT Kanpur
Title: Efficient Bernoulli factory MCMC for intractable posteriors
Abstract: Accept-reject based Markov chain Monte Carlo (MCMC) algorithms have traditionally utilised acceptance probabilities that can be explicitly written as a function of the ratio of the target density at the two contested points. This feature is rendered almost useless in Bayesian posteriors with unknown functional forms. We introduce a new family of MCMC acceptance probabilities that has the distinguishing feature of not being a function of the ratio of the target density at the two points. We present a stable Bernoulli factory that generates events within this class of acceptance probabilities. The efficiency of our methods rely on obtaining reasonable local upper or lower bounds on the target density and we present an application of MCMC on constrained spaces where this is reasonable.
CS Katha Barta hosted by Subhankar Mishra's Lab
Organised by Rucha Joshi and Subhankar Mishra
https://www.niser.ac.in/~smishra/even...