On distributionally regenerative Markov chains

Approximate regenerative-block bootstrap for Markov chains

Regenerative block empirical likelihood for Markov chains

Running Regenerative Markov Chains in Parallel

Parallel computing is playing an increasingly large role in statistical computations. One reason for this is the computational complexity of modern techniques. In this paper, we present a parallel algorithm that provides a considerable speed-up in the running of a large class of Markov chain algorithms. We formally show that it remains theoretically sound, and derive a version of Amdahl’s Law t...

Distributionally Robust Markov Decision Processes

We consider Markov decision processes where the values of the parameters are uncertain. This uncertainty is described by a sequence of nested sets (that is, each set contains the previous one), each of which corresponds to a probabilistic guarantee for a different confidence level so that a set of admissible probability distributions of the unknown parameters is specified. This formulation mode...

Empirical Bayes Estimation in Nonstationary Markov chains

Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...