Liberal Arts and Sciences
Hobert Lab
Led by James Hobert · Statistics
What we study
The Hobert Lab studies statistical methods, particularly Markov chain Monte Carlo (a way to simulate complex statistical models) and generalized linear mixed models (models for data with multiple layers). They develop algorithms to improve these methods.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- Recurrence and transience of a Markov chain on $\mathbb Z$ + and evaluation of prior distributions for a Poisson mean
2024 · Journal of Applied Probability
Studies Markov chains to evaluate improper prior distributions in Poisson models.
- Approximating the Spectral Gap of the Pólya-Gamma Gibbs Sampler
2024 · Methodology And Computing In Applied Probability
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- Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models
2022 · Stochastic Processes and their Applications
- Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions
2022 · Annales de l Institut Henri Poincaré Probabilités et Statistiques
Uses drift and contraction conditions to bound geometric convergence of Markov chains.
- On the convergence rate of the “out-of-order” block Gibbs sampler
2022 · Statistics & Probability Letters
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
James Hobert hasn't set up this page yet. You can also look them up in the UF directory (search “James Hobert”).