meteorological stations usually contain some missing data for different reasons.there are several traditional methods for completing data, among them bivariate and multivariate linear and non-linear correlation analysis, double mass curve, ratio and difference methods, moving average and probability density functions are commonly used. in this paper a blended model comprising the bivariate expo...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TECHNIQUES OF PARALLELIZATION IN MARKOV CHAIN MONTE CARLO METHODS By Vikneswaran Gopal August 2011 Chair: George Casella Major: Statistics Parallel computing is at the forefront of statistical research today. The main reason for this is ...

In this paper, we study the problem of sampling (exactly) uniformly from the set of linear extensions of an arbitrary partial order. Previous Markov chain techniques have yielded algorithms that generate approximately uniform samples. Here, we create a bounding chain for one such Markov chain, and by using a non-Markovian coupling together with a modified form of coupling from the past, we buil...

Stochastic billiards can be used for approximate sampling from the boundary of a bounded convex set through the Markov Chain Monte Carlo (MCMC) paradigm. This paper studies how many steps of the underlying Markov chain are required to get samples (approximately) from the uniform distribution on the boundary of the set, for sets with an upper bound on the curvature of the boundary. Our main theo...

The class of chain graphs (CGs) involving both undirected graphs (=Markov networks) and directed acyclic graphs (= Bayesian networks) was introduced in middle eighties for description of probabilistic conditional independence structures. Every class of Markov equivalent CGs (that is, CGs describing the same conditional independence structure) has a natural representative, which is called the la...

Currently, Markov chain Monte Carlo methods attract much attention among statisticians, cf. e.g. Smith and Roberts (1993), Besag and Green (1993), Besag et al. (1994), Tierney (1994) and the accompaning discussions and references. The genesis of most ideas lies in statistical physics following the early work by Metropolis et al. (1953). In that paper the first Markov chain Monte Carlo algorithm...

In this paper, Markov chain is used to model the reproduction of fixed finite population, and use binomial distribution discuss probability gen inheritance between generations population genes establish transfer matrix ,using conditional mathematics expect obtain absorption at 0 2N only depends on initial fraction A gene, also must be a fixation gene , conclusion same as results MATLAB simulati...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...