In this paper, we propose the first fully polynomial-time randomized approximation scheme (FPRAS) for basic queueing networks, closed Jackson networks with single servers. Our algorithm is based on MCMC (Markov chain Monte Carlo) method. Thus, our scheme returns an approximate solution, of which the size of error satisfies a given error rate. We propose two Markov chains, one is for approximate...

In this paper, we consider Bayesian estimation and prediction problem for the parameters and unobserved lifetimes of exponentiated Weibull distribution based on a progressively type II censored samples. By using an extended likelihood function and informative joint prior distributions, the joint posterior density for the parameters and unobserved lifetimes of units censored at the failure time ...

Biswas et al. [1] introduced a probabilistic approach to inference with limited information in sensor networks. They represented the sensor network as a Bayesian network and performed approximate inference using Markov Chain Monte Carlo (MCMC). The goal is to robustly answer queries even under noisy or partial information scenarios. We propose an alternative method based on simple Monte Carlo e...

Markov chain Monte Carlo methods (MCMC) are essential tools for solving many modern-day statistical and computational problems; however, a major limitation is the inherently sequential nature of these algorithms. In this paper, we propose a natural generalization of the Metropolis-Hastings algorithm that allows for parallelizing a single chain using existing MCMC methods. We do so by proposing ...

Ergodicity of Adaptive MCMC and its Applications Chao Yang Doctor of Philosophy Graduate Department of Statistics University of Toronto 2008 Markov chain Monte Carlo algorithms (MCMC) and Adaptive Markov chain Monte Carlo algorithms (AMCMC) are most important methods of approximately sampling from complicated probability distributions and are widely used in statistics, computer science, chemist...

We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) approximations to jointly sample the current state of the Markov Chain and estimate conditional expectations, possibly by incorporating a full range of variance reduction techniques. We compute Rao-Blackwellized estimates ...

This vignette tests the Markov chain Monte Carlo (MCMC) implementation of Rpackage FME (Soetaert and Petzoldt 2010). It includes the delayed rejection and adaptive Metropolis algorithm (Haario, Laine, Mira, and Saksman 2006)

Tracking articulated figures in high dimensional state spaces require tractable methods for inferring posterior distributions of joint locations, angles, and occlusion parameters. Markov chain Monte Carlo (MCMC) methods are efficient sampling procedures for approximating probability distributions. We apply MCMC to the domain of people tracking and investigate a general framework for sample-appr...

We investigate the use of Markov Chain Monte Carlo (MCMC) methods to attack classical ciphers. MCMC has previously been used to break simple substitution ciphers. Here, we extend this approach to transposition ciphers and to substitution-plus-transposition ciphers. Our algorithms run quickly and perform fairly well even for key lengths as high as 40.

Markov chain Monte Carlo (MCMC) approximates the posterior distribution of latent variable models by generating many samples and averaging over them. In practice, however, it is often more convenient to cut corners, using only a single sample or following a suboptimal averaging strategy. We systematically study different strategies for averaging MCMC samples and show empirically that averaging ...