Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo...

A brief survey of authentication methods in password-protected systems is first provided. Subsequently, features of a freely available software for cracking passwords is then discussed. A randomised procedure for attacking passwords, based on a tilted version of the password distribution, is then proposed. This tilt is a mechanism suggested by large deviations theory. After showing the asymptot...

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis-Hastings (MH) sampler, which can be considered as the atom of the MCMC techn...

Cluster update algorithms dramatically reduce critical slowing down in spin models , but unlike the standard Metropolis algorithm, it is not obvious how to implement these algorithms efficiently on parallel or vector computers. Here we present two different parallel implementations of the Swendsen-Wang algorithm which give reasonable efficiencies on various MIMD parallel computers.

This article provides a brief review of recent developments in Markov chain Monte Carlo methodology. The methods discussed include the standard Metropolis-Hastings algorithm, the Gibbs sampler, and various special cases of interest to practitioners. It also devotes a section on strategies for improving mixing rate of MCMC samplers, e.g., simulated tempering, parallel tempering, parameter expans...

Abstract. I show how Markov chain sampling with the Metropolis-Hastings algorithm can be modified so as to take bigger steps when the distribution being sampled from has the characteristic that its density can be quickly recomputed for a new point if this point differs from a previous point only with respect to a subset of “fast” variables. I show empirically that when using this method, the ef...

The thesis addresses three problems arising from mass spectrometry (MS) data processing. It describes computational methods for solving them and stochastic models that formalize some of them. The first problem is redundancy elimination in liquid chromatography MS (LC-MS) images of peptides. An algorithm for isotopic envelopes detection based on the sweeping method is presented. It consists of g...

The Metropolis–Hastings algorithm was applied to a standard Virtual Population Analysis using a simulated data set containing catch data from 10 age groups over a 10-year period. Posterior distributions of fishing mortalities and population sizes are studied under different prior assumptions. The results demonstrate the usefulness of obtaining distributions of plausible parameter values instead...

Introduction: One major problem in analyzing epidemic data is the lack of data and high dependency among the available data, which is due to the fact that the epidemic process is not directly observable. Methods: One method for epidemic data analysis to estimate the desired epidemic parameters, such as disease transmission rate and recovery rate, is data ...