James P. Hobert

Teaching

• STA 4321
• orientation slides

Research

• publications since 2015
• vita

Papers that have recently appeared:

• Hobert and Khare (2024). Recurrence and transience of a Markov chain on Z^+ and evaluation of prior distributions for a Poisson mean. Journal of Applied Probability pdf

• Davis and Hobert (2024). Approximating the spectral gap of the Pólya-Gamma Gibbs sampler. Methodology & Computing in Applied Probability arXiv

• Jin and Hobert (2022). On the convergence rate of the "out-of-order" block Gibbs sampler, Statistics & Probability Letters arXiv

• Qin and Hobert (2022). Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques arXiv

• Jin and Hobert (2022). Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models, Stochastic Processes and their Applications arXiv

• Qin and Hobert (2022). Wasserstein-based methods for convergence complexity analysis of MCMC with applications, Annals of Applied Probability arXiv

• Davis and Hobert (2021). On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models, Methodology & Computing in Applied Probability arXiv

• Qin and Hobert (2021). On the limitations of single-step drift and minorization in Markov chain convergence analysis, Annals of Applied Probability arXiv

• Backlund, Hobert, Jung and Khare (2021). A hybrid scan Gibbs sampler for Bayesian models with latent variables, Statistical Science arXiv

• Backlund and Hobert (2020). A note on the convergence rate of MCMC for robust Bayesian multivariate linear regression with proper priors, Computational and Mathematical Methods pdf