On the Markov chain central limit theorem
Galin L. Jones
Abstract
The goal of this expository paper is to describe conditions which guarantee a central limit theorem for functionals of general state space Markov chains. This is done with a view towards Markov chain Monte Carlo settings and hence the focus is on the connections between drift and mixing conditions and their implications. In particular, we consider three commonly cited central limit theorems and discuss their relationship to classical results for mixing processes. Several motivating examples are given which range from toy one-dimensional settings to complicated settings encountered in Markov chain Monte Carlo.
Create a lesson
Related papers
Boolean Small-Ball Inequalities for Discrepancy Theory
Emrullah Akbas, Suvrit Sra
Markovian renormalisation for percolation in high-dimension: Semi-decidability of mean field behavior
Arthur Blanc-Renaudie
Point process convergence of large inradii of Poisson-Laguerre tessellations
Matthias Schulte, Martina Švarc Petráková
Interpolation of Gaussian Free Fields via Random Matrices
Gabriel Raposo
Almost-Uniform Bayesian Convergence to the Truth Is Not Characterized by Countable Additivity on Conditional Hitting Times
M. Ali Khan, Arthur Paul Pedersen, Maxwell B. Stinchcombe
The skeleton-blocks decomposition of Bienaymé trees, and applications to their local convergence
Marc Bernard, Robin Stephenson