A method to reduce the rejection rate in Monte Carlo Markov chains

A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains

We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random and unbounded. The method simulates n copies of the chain in parallel, using a (d + 1)-dimensional highly-uniform point set of cardinality n, randomized independently at each step, where d is the number of uniform rando...

Ordering Monte Carlo Markov Chains

Markov chains having the same stationary distribution can be partially ordered by performance in the central limit theorem. We say that one chain is at least as good as another in the e ciency partial ordering if the variance in the central limit theorem is at least as small for every L( ) functional of the chain. Peskun partial ordering implies e ciency partial ordering [25, 30]. Here we show ...

Slice Samoling as a Markov Chain Monte Carlo Method

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Parallel and interacting Markov chains Monte Carlo method

Lecture 18: Monte Carlo and Markov Chains

For Halloween, we come as a math course 1 Monte Carlo Integration Suppose we want to evaluate a definite integral,