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...

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 ...

Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn these models exactly, various approximate learning techniques have been developed, such as contrastive divergence (CD) and Markov chain Monte Carlo maximum likelihood estimation (MCMC-MLE). In this paper, we introduce...

Markov random "eld (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result. ( 2000 Pattern Recognition Society. Published by Else...

This article presents a mathematical paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for object recognition. The obje ctives of this paradigm are two-fold. Firstly, it realizes traditional \hyp othesis-and-test"methods through wellbalanced Markov chain monte Carlo (MCMC) dynamics, thus it achieves robust and globally optimal solutions. Se condly, it utilizes data-driven (bottom-up...

The segmentation of an image can be presented as an inverse ill-posed problem. The segmentation problem is presented as, knowing an observed image g, how to obtain an original image f in which a classification in statistically homogeneous regions must be established. The inversion technique we use is done in a Bayesian probabilistic framework. Prior hypothesis, made on different parameters of t...

As the many examples in this book illustrate, Markov chain Monte Carlo (MCMC) methods have revolutionized Bayesian statistical analyses. Rather than using off-the-shelf models and methods, we can use MCMC to fit application specific models that are designed to account for the particular complexities of a problem at hand. These complex multilevel models are becoming more prevalent throughout the...

Predictions in subsurface formations consists of two steps: characterization and prediction using the characterization. In the characterization, we reconstruct the subsurface properties, such as distributions of permeability and porosity, with a set of limited data. A Bayesian approach using Markov Chain Monte Carlo (MCMC) methods is well suited for reconstructing permeability and porosity fiel...

Subset Simulation is an adaptive simulation method that efficiently solves structural reliability problems with many random variables. The method requires sampling from conditional distributions, which is achieved through Markov Chain Monte Carlo (MCMC) algorithms. This paper discusses different MCMC algorithms proposed for Subset Simulation and introduces a novel approach for MCMC sampling in ...