6 citations · 26 across the 20 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…