25 citations · 44 across the 16 of their papers we have counts for
19 papers
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo
James A. Brofos, Vivekananda Roy, Roy R. Lederman
Riemannian manifold Hamiltonian (RMHMC) and Lagrangian Monte Carlo (LMC) have emerged as powerful methods of Bayesian inference. Unlike Euclidean Hamiltonian Monte Carlo (EHMC) and…
MCMC for GLMMs
Vivekananda Roy
Generalized linear mixed models (GLMMs) are often used for analyzing correlated non-Gaussian data. The likelihood function in a GLMM is available only as a high dimensional integra…
Convergence of position-dependent MALA with application to conditional simulation in GLMMs
Vivekananda Roy, Lijin Zhang
We establish conditions under which Metropolis-Hastings (MH) algorithms with a position-dependent proposal covariance matrix will or will not have the geometric rate of convergence…
Analyzing Relevance Vector Machines using a single penalty approach
Anand Dixit, Vivekananda Roy
Relevance vector machine (RVM) is a popular sparse Bayesian learning model typically used for prediction. Recently it has been shown that improper priors assumed on multiple penalt…
Bayesian iterative screening in ultra-high dimensional linear regressions
Run Wang, Somak Dutta, Vivekananda Roy
Variable selection in ultra-high dimensional linear regression is often preceded by a screening step to significantly reduce the dimension. Here we develop a Bayesian variable scre…
Block Gibbs samplers for logistic mixed models: convergence properties and a comparison with full Gibbs samplers
Yalin Rao, Vivekananda Roy
The logistic linear mixed model (LLMM) is one of the most widely used statistical models. Generally, Markov chain Monte Carlo algorithms are used to explore the posterior densities…