Parallel and interacting Markov chain Monte Carlo algorithm

An Adaptive Interacting Wang–Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area that we feel deserves much further attention. Toward this aim, this article proposes a general-purpose algorithm f...

A Stochastic algorithm to solve multiple dimensional Fredholm integral equations of the second kind

In the present work‎, ‎a new stochastic algorithm is proposed to solve multiple dimensional Fredholm integral equations of the second kind‎. ‎The solution of the‎ integral equation is described by the Neumann series expansion‎. ‎Each term of this expansion can be considered as an expectation which is approximated by a continuous Markov chain Monte Carlo method‎. ‎An algorithm is proposed to sim...

Sequentially Interacting Markov Chain Monte Carlo Methods

We introduce a novel methodology for sampling from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. These problems are usually addressed using Sequential Monte Carlo (SMC) methods. The alternative Sequentially Interacting Markov Chain Monte Carlo (SIMCMC) scheme proposed here works by generating interacting non-Markovian sequences which...

Parallel and interacting Markov chains Monte Carlo method

Parallel Monte Carlo Methods

This paper is the key paper in my Survey-based term project. The author argues that Monte Carlo algorithms are ideally suited to parallel computing, and that “parallel Monte Carlo” should be more widely used. Therefore I consider many of issues can be raised by use Parallel Monte Carlo method. And from this paper I also can discuss the possibilities of parallel Markov chain Monte Carlo which is...