# Tag Archives: Markov chain Monte Carlo

## Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo

In a sense, this paper is an extension of the paper

In this paper we prove bounds on the discrepancy of Markov chains which are generated by a deterministic driver sequence $u_1, u_2, \ldots, u_n \in [0,1]^s$. We also prove a Koksma-Hlawka inequality. The main assumption which we use is uniform ergodicity of the transition (Markov) kernel. We describe the essential ingredients and results in the following. Continue reading

## Consistency of Markov Chain quasi-Monte Carlo for continuous state spaces

This post is based on the paper

and my previous talks on this topic at the UNSW statistic seminar and the Dagstuhl Workshop in 2009. The slides of my talk at Dagstuhl can be found here. I give an illustration of the results rather than rigorous proofs, which can be found in the paper [CDO].

The classical paper on this topic is by [Chentsov 1967]:

N. N. Chentsov, Pseudorandom numbers for modelling Markov chains, Computational Mathematics and Mathematical Physics, 7, 218–2332, 1967.

Further important steps were taken by A. Owen and S. Tribble, see doi: 10.1073/pnas.0409596102 and doi: 10.1214/07-EJS162. Recent papers of interest in this context are also by A. Hinrichs doi:10.1016/j.jco.2009.11.003, and D. Rudolf doi: 10.1016/j.jco.2008.05.005. The slides of the presentations at Dagstuhl of Hinrichs can be found here here and of Rudolf can be found here.

1. Introduction