8 papers
Scalable Bayesian inference for high-dimensional mixed-type multivariate spatial data
Arghya Mukherjee, Arnab Hazra, Dootika Vats
Spatial generalized linear mixed-effects models are popularly used to analyze spatially indexed univariate responses. However, with modern technology, it is common to observe vecto…
Exact MCMC for Intractable Proposals
Dwija Kakkad, Dootika Vats
Accept-reject based Markov chain Monte Carlo (MCMC) methods are the workhorse algorithm for Bayesian inference. These algorithms, like Metropolis-Hastings, require choosing a propo…
Hamiltonian Monte Carlo for (Physics) Dummies
Arghya Mukherjee, Dootika Vats
Sampling-based inference has seen a surge of interest in recent years. Hamiltonian Monte Carlo (HMC) has emerged as a powerful algorithm that leverages concepts from Hamiltonian dy…
Solving the Poisson equation using coupled Markov chains
Randal Douc, Pierre E. Jacob, Anthony Lee +1
This article shows how coupled Markov chains that meet exactly after a random number of iterations can be used to generate unbiased estimators of the solutions of the Poisson equat…
Proximal Hamiltonian Monte Carlo
Apratim Shukla, Dootika Vats, Eric C. Chi
Bayesian formulation of modern day signal processing problems has called for improved Markov chain Monte Carlo (MCMC) sampling algorithms for inference. The need for efficient samp…
MCMC Importance Sampling via Moreau-Yosida Envelopes
Apratim Shukla, Dootika Vats, Eric C. Chi
Non-differentiable priors are standard in modern parsimonious Bayesian models. Lack of differentiability, however, precludes gradient-based Markov chain Monte Carlo (MCMC) for post…