This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compi...

Belief propagation is a powerful procedure to perform inference, and exhibits near-optimal performance in iterative decoding/demapping. However, a complete analysis of the iterative process is still lacking, and convergence of the procedure has not proved in general cases. Information geometry offers a new view from differential geometry to understand this problem. In this thesis, we try to und...

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...

Expert search is a task of growing importance in Enterprise settings. In a classical search setting, users normally require relevant documents to fulfil an information need. However, in Enterprise settings, users also have a need to identify the co-workers with relevant expertise to a topic area. An expert search engine assists users with their expertise need, by ranking candidate experts with ...

The paper presents a new method and the corresponding program of presentation of bayesian belief networks. The belief network can be viewed and updated via World Wide Web. Consistency checks are possible. Edge removal and insertion operations are done in anìntelligent way' that is corrections of valuations are carried out automatically in a user-friendly way. The corresponding program is implem...

Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets' {Smets:93} directed ac...

Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In ...

We describe how to combine probabilistic logic and Bayesian networks to obtain a new framework (\Bayesian logic") for dealing with uncertainty and causal relationships in an expert system. Probabilistic logic, invented by Boole, is a technique for drawing inferences from uncertain propositions for which there are no independence assumptions. A Bayesian network is a \belief net" that can represe...

We have created a logic-based, first-order, and Turingcomplete set of software tools for stochastic modeling. Because the inference scheme for this language is based on a variant of Pearl's loopy belief propagation algorithm, we call it Loopy Logic. Traditional Bayesian belief networks have limited expressive power, basically constrained to that of atomic elements as in the propositional calcul...

Several philosophers and psychologists have characterized belief in conspiracy theories as a product of irrational reasoning. Proponents apparently resist revising their beliefs given disconfirming evidence tend to believe more than one conspiracy, even when the relevant are mutually inconsistent. In this paper, we bring leading views on theoretic closer together by exploring rationality under ...