A maximal prime subgraph decomposition junction tree (MPD-JT) is a useful computational structure that facilitates lazy propagation in Bayesian networks (BNs). A graphical method was proposed to construct an MPD-JT from a BN. In this paper, we present a new method from a relational database (RDB) perspective which sheds light on the semantic meaning of the previously proposed graphical algorithm.

Network reconstruction methods are commonly used in molecular biology to construct regulatory networks from information about the expression values of genes in a cell. In ecology, we are presented with a similar problem when trying to reconstruct species interaction networks based on species abundance data. The aim of this project was to see if the methods that proved successful in molecular bi...

This paper presents a new hybrid approach for learning Bayesian networks (BNs) based on artificial bee colony algorithm and particle swarm optimization. Firstly, an unconstrained optimization problem is established, which can provide a smaller search space. Secondly, the definition and encoding of the basic mathematical elements of our algorithm are given, and the basic operations are designed,...

As large-scale sensor networks are being deployed with the objective of collecting quality data to support user queries and decision-making, the role of a scalable query model becomes increasingly critical. An effective query model should scale well with large network deployments and address user queries at specified confidence while maximizing sensor resource conservation. In this paper, we pr...

Bayesian networks (BNs) are an essential tool for the modeling of cognitive processes. They represent probabilistic knowledge in an intuitive way and allow to draw inferences based on current evidence and built-in hypotheses. In this paper, a structure learning scheme for BNs will be examined that is based on so-called Child-friendly Parent Divorcing (CfPD). This algorithm groups together nodes...

EFFICIENT INFERENCE FOR HYBRID BAYESIAN NETWORKS Wei Sun, PhD George Mason University, 2007 Dissertation Director: Dr. KC Chang Uncertainty is everywhere in real life so we have to use stochastic model for most real-world problems. In general, both the systems mechanism and the observable measurements involve random noise. Therefore, probability theory and statistical estimation play important ...

Bayesian networks (BNs) are powerful tools for knowledge representation and inference that encode (in)dependencies among random variables. A Bayesian network classifier is a special kind of these networks that aims to compute the posterior probability of each class given an instance of the attributes and predicts the class with the highest posterior probability. Since learning the optimal BN st...

Apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. However, ontologies are not capable of representing uncertainty and the effective detection of an...

Multi-agent systems (MAS) are groups of interacting intelligent software agents. An important application is sensor interpretation (SI) in sensor networks. SI domains are frequently modeled with Bayesian networks (BNs), and distributed versions of these problems can be modeled with distributed Bayesian networks (DBNs). The multiply sectioned Bayesian network (MSBN) framework is the most studied...

One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we pro...