Multiply sectioned Bayesian networks (MSBNs) were originally proposed as a modular representation of uncertain knowledge by sectioning a large Bayesian network (BN) into smaller units. More recently, hierarchical Markov networks (HMNs) were developed in part as an hierarchical representation of the flat BN. In this paper, we compare the MSBN and HMN representations. The MSBN representation does...

We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche’s approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBN...

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...

In this paper, several Artificial Intelligence (AI) techniques such as Rule-Based Reasoning (RBR), Bayesian Networks (BNs), Neural Networks (NNs), Case-Based Reasoning (CBR), Qualitative Reasoning (QR), and Model-Based Reasoning (MBR) are described. Then an automated management system prototype is presented. Finally, a hybrid approach to automated network and system management is proposed. Howe...

Bayesian networks (BNs) are a key method for representation and reasoning under uncertainty in artificial intelligence. Both exact and approximate BN inference have been proven to be NP-hard. The problems of inference become even less tractable under real-time constraints. One solution to real-time AI problems is to develop anytime algorithms. Anytime algorithms are iterative refinement algorit...

Darwinian networks (DNs) are introduced to simplify and clarify working with Bayesian networks (BNs). Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model. The graphical manipulation of the tables then takes on a biological feel. It is shown how DNs can unify modeling and reasoning tasks into a single platform.

a new structure learning approach for bayesian networks (bns) based on asexual reproduction optimization (aro) is proposed in this letter. aro can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. in aro, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

Bayesian networks (BNs) are an appealing model for causal and noncausal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent) variables. Here, we investigate the prospects of Bayesian learning of ancestor relations, including arcs, in the ...

In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples. We show that the minimum number of samples required by any procedure to recover the correct structure grows as Ω (m) and Ω (k logm+ k/m) for non-sparse and sparse BNs respectively, where m is the numbe...

Existing methods of parameter and structure learning of Bayesian Networks (BNs) from a database assume that the database is complete. If there are missing values, they are assumed to be missing at random. This paper incorporates the concepts used in Dempster-Shafer theory of belief functions to learn both the parameters and structure of BNs. Instead of filling the missing values by their estima...