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.

• Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].

An MCMC(Markov Chain Monte Carlo) algorithm is proposed for nonlinear time series prediction with Hierarchical Bayesian framework. The algorithm computes predictive mean and error bar by drawing samples from predictive distributions. The algorithm is tested against time series generated by (chaotic) Rössler system and it outperforms quadratic approximations previously proposed by the authors.

In this contribution we discuss a stochastic framework for air traffic conflict resolution. The conflict resolution task is posed as the problem of optimizing an expected value criterion. Optimization is carried out by Monte Carlo Markov Chain (MCMC) simulation. A numerical example illustrates the proposed strategy. Copyright c © 2005 IFAC

We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are...

In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search. The experiments will be carried out on three real life credit scoring data sets. It will be shown that M...

We connect known results about diffusion limits of Markov chain Monte Carlo (MCMC) algorithms to the computer science notion of algorithm complexity. Ourmain result states that any weak limit of a Markov process implies a corresponding complexity bound (in an appropriate metric). We then combine this result with previously-known MCMC diffusion limit results to prove that under appropriate assum...

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences t...

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon E cient Importance Sampling (EIS) which can be used for the analysis of a wide range of econometric models involving integrals without an analytical solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are chosen to be global approximations to ...

In this article, we propose new Monte Carlo methods for computing a single marginal likelihood or several marginal likelihoods for the purpose of Bayesian model comparisons. The methods are motivated by Bayesian variable selection, in which the marginal likelihoods for all subset variable models are required to compute. The proposed estimates use only a single Markov chain Monte Carlo (MCMC) ou...