A particular Markov chain Monte Carlo algorithmis constructed to allow Bayesian inference in a hidden Markov model used in hematology. The algorithm has an outer Gibbsian structure, and incorporates both Metropolis and Hastings updates to move through the space of possible hidden states. While somewhat sophisticated, this algorithm still has problems getting around the infinite-dimensional spac...

The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. vVe describe the basic LaplaceMetropolis estimator for models without random effects. For models w...

In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large–scale applications with high dimensional parameter spaces, e.g. in uncertainty quantification in porous media flow. We propose a new multilevel Metropolis-Hastings algorithm, and give an abstract, problem dependent theorem on the cost of the new multilevel es...

In this paper we study the ergodicity properties of some adaptive Markov chain Monte Carlo algorithms (MCMC) that have been recently proposed in the literature. We prove that under a set of verifiable conditions, ergodic averages calculated from the output of a so-called adaptive MCMC sampler converge to the required value and can even, under more stringent assumptions, satisfy a central limit ...

the informality of possession and lack of security in residence are among the characteristics attributed to the informal settlements, as well as, they are a key to other problems’ solutions. this essay investigates some of the typical cases of iran’s informal settlements, and this intends to find out the possession conditions, as well as, the conditions necessary for the recognition of the ...

noise pollution can be considered as the most important problem of urban environmental management in mega metropolises and urban transportation is the main source of the mentioned problem. this study aims to investigate the amount of traffic noise dose received by traffic policemen in tehran; district 1 municipality. the investigation was conducted during three consecutive months in 2007 in 282...

Umacs (Universal Markov chain sampler) is an R software package that facilitates the construction of the Gibbs sampler and Metropolis algorithm for Bayesian inference. Umacs is a practical tool to write samplers in R. This is sometimes necessary for large problems that cannot be fit using programs like BUGS. The user supplies the data, parameter names, updating functions, and a procedure for ge...

Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implic...