These notes are intended to provide the reader with knowledge of basic concepts of Markov chain Monte Carlo (MCMC) and hopefully also some intuition about how MCMC works. For more thorough accounts of MCMC the reader is referred to e.g. Gilks et al. (1996), Gamerman (1997), or Robert and Casella (1999). Suppose that we are interested in generating samples from a target probability distribution ...

This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed techniq...

in recent years, some statisticians have studied the signal detection problem by using the random field theory. in this paper we have considered point estimation of the gaussian scale space random field parameters in the bayesian approach. since the posterior distribution for the parameters of interest dose not have a closed form, we introduce the markov chain monte carlo (mcmc) algorithm to ap...

We present a versatile Monte Carlo method for estimating multidimensional integrals, with applications to rare-event probability estimation. The method fuses two distinct and popular Monte Carlo simulation methods — Markov chain Monte Carlo and importance sampling — into a single algorithm. We show that for some illustrative and applied numerical examples the proposed Markov Chain importance sa...

Steepest Descent. Discrete Iterative Optimization. Markov Chain Monte Carlo (MCMC). NOTE: NOT FOR DISTRIBUTION!!

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...

Markov chain Monte Carlo (MCMC) simulations are modeled as driven by true random numbers. We consider variance bounding Markov chains driven by a deterministic sequence of numbers. The star-discrepancy provides a measure of efficiency of such Markov chain quasi-Monte Carlo methods. We define a pull-back discrepancy of the driver sequence and state a close relation to the star-discrepancy of the...

Existing Markov Chain Monte Carlo (MCMC) methods are either based on generalpurpose and domain-agnostic schemes, which can lead to slow convergence, or problem-specific proposals hand-crafted by an expert. In this paper, we propose ANICE-MC, a novel method to automatically design efficient Markov chain kernels tailored for a specific domain. First, we propose an efficient likelihood-free advers...