background : to prepare safe and adequate blood supply to meet patients’ needs and ensure a sufficient number of regular blood donors, knowledge about factors encouraging people to donate blood regularly is essential. considering its importance, we aimed to identifying the effective factors of the return to blood donation in based on zero-inflated count regression models using bayesian approach...

We shortly review the uncoupling-coupling method, a Markov chain Monte Carlo based approach to compute statistical properties of systems like medium-sized biomolecules. This technique has recently been proposed for the efficient computation of biomolecular conformations. One crucial step of UC is the decomposition of reversible nearly uncoupled Markov chains into rapidly mixing subchains. We sh...

New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough present...

In this paper, a Monte Carlo simulation is performed to investigate the finite sample properties of various estimators, based on discretely sampled observations, of the continuous-time Itô diffusion process. The simulation study aims to compare the performance of the nonparametric estimators proposed in Jiang and Knight (1996) with common parametric estimators based on those diffusion processes...

When fitting a model to any data, there is some uncertainty about which model is best. Green (1995) quantifies this uncertainty through the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. When using the RJMCMC method in a regime-switching situation, the chain determines the optimal number of regimes by jumping between various possibilities. This method gives each model its posterior p...

Finite Poisson mixtures are widely used to model overdispersed data sets for which the simple Poisson distribution is inadequate. Such data sets are very common in real applications. In this paper we investigate Bayesian estimation via MCMC for finite Poisson mixtures and we discuss some computational issues. The related problem of determining the number of components in a mixture is also treat...

This paper presents a novel approach for characterizing wideband (CDMA) multiple dimensional channels for the wireless environment in arbitrarily coloured additive Gaussian noise. This characterization is sufficient for the specification of optimal multichannel space-time receivers. The proposed solution is defined in the Bayesian framework and uses the Reversible Jump Markov Chain Monte Carlo ...

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...

Sequential Monte Carlo Samplers are a class of stochastic algorithms for Monte Carlo integral estimation w.r.t. probability distributions, which combine elements of Markov chain Monte Carlo methods and importance sampling/resampling schemes. We develop a stability analysis by functional inequalities for a nonlinear flow of probability measures describing the limit behavior of the algorithms as ...

We provide a short overview of Importance Sampling – a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations. We review the fundamental developments in designing efficient IS for practical use. This includes parametric approximation with optimization based adaptation, sequential sampl...