A Bayesian model selection for modelling a time series by an autoregressive–moving–average model (ARMA) is presented. The posterior distribution of unknown parameters and the selected orders are obtained by the Markov chain Monte Carlo (MCMC) method. An MCMC algorithm that represents the parameters of the model as a point process has been implemented. The method is illustrated on simulated seri...

In an effort to extend the tempering methodology, we propose simulated sintering as a general framework for designing Markov chain Monte Carlo algorithms. To implement sintering, one identifies a family of probability distributions, all related to the target one and defined on spaces of different dimensions. Then, a Markov chain is constructed to move across these spaces, with the hope that the...

Abstract. Bayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realizations of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains interact in ord...

This paper presents a new glottal inverse filtering (GIF) method that utilizes Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, the iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Klatt model and filtered with the estimated vo...

Richardson and Green (1997) present a method of performing a Bayesian analysis of data from a finite mixture distribution with an unknown number of components. Their method is a Markov Chain Monte Carlo (MCMC) approach, which makes use of the “reversible jump” methodology described by Green (1995). We describe an alternative MCMC method which views the parameters of the model as a (marked) poin...

In the following paper we investigate simulation methodology for Bayesian inference in Lévy driven SV models. Typically, Bayesian inference from such statistical models is performed using Markov chain Monte Carlo (MCMC) methods. However, it is well-known that fitting SV models using MCMC is not always straight-forward. One method that can improve over MCMC is SMC samplers ([14]), but in that ap...

In radar tracking application, the observation noise is highly non-Gaussian, which is also referred as glint noise. The performance of extended Kalman filter degrades severely in the presence of glint noise. In this paper, an improved particle filter, Markov chain Monte Carlo particle filter (MCMC-PF), is introduced to cope with radar target tracking in glint noise environment. The Monte Carlo ...

This paper proposes a software reliability model (SRM) based on a mixed gamma distribution, so-called the mixed gamma SRM. In addition, we develop the parameter estimation method for the mixed gamma SRM. Concretely, the estimation method is based on the Bayesian estimation and the parameter estimation algorithm is described by MCMC (Markov chain Monte Carlo) method with grouped data.

In this note we attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today. We see how the earlier stages of the Monte Carlo (MC, not MCMC) research have led to the algorithms currently in use. More importantly, we see how the development of this methodology has not only changed our solutions to problems, but...

Bayesian credible bounds produced from Markov chain Monte Carlo (MCMC) procedures contain Monte Carlo error and thus may require a long chain in order to have a reasonable degree of repeatability. This is especially true when there is a substantial amount of autocorrelation in the chain realization. Repeatability would be important in some applications where it would be undesirable to report nu...