One of the main problems in credit risk management is the correlated default. In large portfolios, computing the default dependencies among issuers is an essential part in quantifying the portfolio's credit. The most important problems related to credit risk management are understanding the complex dependence structure of the associated variables and lacking the data. This paper aims at introdu...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

This paper incorporates Belief Propagation into an instance of Estimation of Distribution Algorithms called Estimation of Bayesian Networks Algorithm. Estimation of Bayesian Networks Algorithm learns a Bayesian network at each step. The objective of the proposed variation is to increase the search capabilities by extracting information of the, computationally costly to learn, Bayesian network. ...

In risk analysis based on Bayesian framework, premium calculation requires&nbsp;specification of a prior distribution for the risk parameter in the heterogeneous portfolio. When the prior knowledge is vague, the E-Bayesian and robust Bayesian analysis&nbsp;can be used to handle the uncertainty in specifying the prior distribution by considering a class of priors instead of a single prior. In th...

this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

animal models are used to model the observations of animal performance that are genetically dependent.these models are considered as generalized linear mixed models and the genetic correlation structure of data is considered through random effects of breeding values. one goal of the mentioned models is to estimate variance components. in this research, an approximate bayesian approach presented...

Bayesian ability estimation is a statistical inferential framework constructed from a measurement model and a prior knowledge model. It is attractive in practice because Bayesian estimation methods offer an elegant way to incorporate appropriate knowledge on target ability distribution in order to improve the accuracy of ability estimation, when there are uncertainties or errors in observable d...