This paper proposes a semi-parametric 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 ...

A class of semi-parametric hazard/failure rates with a bathtub shape is of interest. It does not only provide a great deal of flexibility over existing parametric methods in the modeling aspect but also results in a closed-form and tractable Bayes estimator for the bathtub-shaped failure rate. Such an estimator is derived to be a finite sum over two S-paths due to an explicit posterior analysis...

This work introduces a Bayesian semi-parametric approach for dealing with regression models where the covariate is measured with error. The main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in the dependent and independent situations. Conditional posterior distributions are imp...

Various aspects of Bayesian inference in selection and size biased sampling problems are presented beginning with discussion of general problems of inference in in nite and nite populations subject to selection sampling Estimation of the size of nite populations and inference about superpopulation distributions when sampling is apparently informative is then developed in two speci c problems Th...

This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non-parametric approaches for GARCH models that avoid imposing arbitrary pa...

A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown parent datum. The latent graph structure is assumed to lie in the family of directed out-tree graphs which leads to efficient Bayesian inference. The latent likelihood of the data and its gradients are computable in clo...

In multiclass semi-supervised learning (SSL), it is sometimes the case that the number of classes present in the data is not known, and hence no labeled examples are provided for some classes. In this paper we present variants of well-known semi-supervised multiclass learning methods that are robust when the data contains an unknown number of classes. In particular, we present an “exploratory” ...