In this paper, we focus on diagnosis in distributed computer systems using end-to-end transactions, or probes. Diagnostic problem is formulated as a probabilistic inference in a bipartite noisy-OR Bayesian network. Due to general intractability of exact inference in such networks, we apply belief propagation (BP), a popular approximation technique proven successful in various applications, from...

In this paper, we claim that software development will do well by explicit modeling of its uncertainties using existing uncertainty modeling techniques. This is accomplished initially by stating the Maxim of Uncertainty in Software Engineering (MUSE), followed by a detailed presentation of uncertainty in software testing. We then propose that a speciic technique, known as Bayesian Belief Networ...

MOTIVATION The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of geno...

Recent advances in communications, mobile computing, and artificial intelligence have greatly expanded the application space of intelligent distributed sensor networks. This in turn motivates the development of generalized Bayesian decentralized data fusion (DDF) algorithms for robust and efficient information sharing among autonomous agents using probabilistic belief models. However, DDF is si...

In this paper, we claim that software development will do well by explicit modeling of its uncertainties using existing uncertainty modeling techniques. This is accomplished initially by stating the Maxim of Uncertainty in Software Engineering (MUSE), followed by a detailed presentation of uncertainty in software testing. We then propose that a speci c technique, known as Bayesian Belief Networ...