## James P. Hobert

## Teaching

## Research

### Submissions:

- Davis and Hobert (2021). Approximating the spectral gap of the
Pólya-Gamma Gibbs
sampler. arXiv

### Accepted papers that have yet to appear:

- 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

### Papers that have recently appeared:

- 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