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Persi Diaconis is Professor of Mathematics and Statistics at Stanford University. He works in the mathematics and practice of Bayesian statistics (conjugacy, consistency properties of Dirichlet priors, deFinetti's theorem). In rates of convergence for Markov chains to stationality (how many times to shuffle cards, understanding the Metropolis algorithm, geometric theory of Markov chains). He also works in combinatorics, group theory, and random matrix theory and in the philosophy of probability.
Bayesian Analysis of Markov Chains
as author at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009,
Bayesian Numerical Analysis
as author at Probabilistic Numerics,
Building apriori knowledge into conclusions drawn from simulations
as author at International Society for Bayesian Analysis (ISBA), Sardinia 2016,