Galin Jones

School of Statistics		Office: 347 Ford Hall
University of Minnesota		Email:galin AT stat DOT umn DOT edu
224 Church Street S. E.		Phone: 612.624.8862
Minneapolis, MN 55455		Fax: 612.624.8868

Research

Jones GL and Johnson AA. Discussion of ``Gibbs sampling, exponential families, and orthogonal polynomials'' by Diaconis, Khare, and Saloff-Coste. To appear in Statistical Science. pdf

Johnson AA and Jones GL. Gibbs Sampling for a Bayesian Hierarchical Version of the General Linear Mixed Model arXiv

Flegal JM, Haran M, and Jones GL. Markov chain Monte Carlo: Can we trust the third significant figure? To appear in Statistical Science. arXiv

Eaton ML, Hobert JP, Jones GL and Lai W-L. Evaluation of formal posterior distributions via Markov chain arguments. To appear in The Annals of Statistics. arXiv extended version

Eaton ML, Hobert JP and Jones GL. On perturbations of strongly admissible prior distributions. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques paper pdf talk pdf

Jones GL, Haran M,Caffo BS and Neath R. Fixed-width output analysis for Markov chain Monte Carlo. Journal of the American Statistical Association. arXiv, talk pdf and R program for consistent batch means can be found here.

Hobert JP, Jones GL and Robert CP. Using a Markov chain to construct a tractable approximation of an intractable probability distribution. Scandinavian Journal of Statistics. paper pdf and talk

Caffo BS, Jank W and Jones GL. Ascent-Based Monte Carlo EM. Journal of the Royal Statistical Society Series B. paper pdf and talk pdf

Jones GL. On the Markov chain central limit theorem. Probability Surveys. arXiv

Jones GL and Hobert JP. Sufficient burn-in for Gibbs samplers for a hierarchical random effects model. The Annals of Statistics. arXiv

Hobert JP, Jones GL, Presnell B and Rosenthal JS. On the applicability of regenerative simulation in Markov chain Monte Carlo. Biometrika. pdf

Jones GL and Hobert JP. Honest exploration of intractable probability distributions via Markov chain Monte Carlo. Statistical Science. pdf

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