Supply chain risk management is an active area of research and there is a research gap of exploring established risk quantification techniques in other fields for application in the context of supply chain management. We have developed a novel framework for quantification of supply chain risks that integrates two techniques of Bayesian belief network and Game theory. Bayesian belief network can...

This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with nitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intr...

Introduction: Diagnosis of high-risk maternal pregnancy is one the most important issues during and can be great help to pregnant mothers. Also, early diagnosis reduce mortality morbidity in mothers.Material Methods: In this study, data 1014 mothers were used, which includes 272 people with pregnancies, 742 medium-risk low-risk pregnancies. include six independent variables. A combination Bayes...

background: high-risk unsafe behaviors (hrubs) have been known as the main cause of occupational accidents. considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. the aim of the present study was to find strategies for decreasing the rate of hrubs using an integrated approach of s...

In this paper the behavior of various be­ lief network learning algorithms is stud­ ied. Selecting belief networks with cer­ tain minimallity properties turns out to be NP-hard, which justifies the use of search heuristics. Search heuristics based on the Bayesian measure of Cooper and Her­ skovits and a minimum description length (MDL) measure are compared with re­ spect to their properties for...

The proportion of elderly in the population is continuing to increase, placing additional demands on highly competitive medical budgets. The management of the care of the elderly within hospitals can be assisted by the accurate modelling of the length of stay of patients in hospital. This paper uses conditional phase-type distributions for modelling the length of stay of a group of elderly pati...

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. The...

We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in the case of inconsistent qualitative knowledge. A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features. Each inconsistent knowledge c...