An introduction to computational complexity in Markov Chain Monte Carlo methods

The aim of this work is to give an introduction to the theoretical background and computational complexity of Markov chain Monte Carlo methods. Most of the mathematical results related to the convergence are not found in most of the statistical references, and computational complexity is still an open question for most of the MCMC methods. In this work, we provide a general overview, references, and discussion about all these theoretical subjects.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/22/2019

Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé

This is a contribution for the discussion on "Unbiased Markov chain Mont...
research
02/22/2020

Markov Chain Monte-Carlo Phylogenetic Inference Construction in Computational Historical Linguistics

More and more languages in the world are under study nowadays, as a resu...
research
08/29/2016

On the Computational Complexity of Geometric Langevin Monte Carlo

Manifold Markov chain Monte Carlo algorithms have been introduced to sam...
research
10/21/2019

Aggregated Gradient Langevin Dynamics

In this paper, we explore a general Aggregated Gradient Langevin Dynamic...
research
07/12/2021

Mathematical Analysis of Redistricting in Utah

We investigate the claim that the Utah congressional districts enacted i...
research
05/13/2022

On the use of a local R-hat to improve MCMC convergence diagnostic

Diagnosing convergence of Markov chain Monte Carlo is crucial and remain...
research
03/17/2022

Evaluating Posterior Distributions by Selectively Breeding Prior Samples

Using Markov chain Monte Carlo to sample from posterior distributions wa...

Please sign up or login with your details

Forgot password? Click here to reset