Multiply sectioned Bayesian networks (MSBNs) provide a coherent and flexible formalism for representing uncertain knowledge in large domains. Global consistency among subnets in a MSBN is achieved by communication. When a subnet updates its belief with respect to an adjacent subnet, existing inference operations require repeated belief propagations (proportional to the number of linkages betwee...

Many decisions, of an entrepreneurial as well as an institutional or personal character, have to be made on the basis of incomplete or partial information about the outcomes and results and, hence, under conditions of rist and uncertainty. Among the numerous proposals for dealing with uncertainty Bayesianism has gained a prominent role. Although originally founded in decision theory, where for ...

Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria f...

This paper describes a new framework for using natural selection to evolve Bayesian Networks for use in forecasting time series data. It extends current research by introducing a tree based representation of a candidate Bayesian Network that addresses the problem of model identification and training through the use of natural selection. The framework constructs a modified Naïve Bayesian classif...

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 ...

In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constrain...