Bayesian networks provide an attractive representation of structured probabilistic information. There is thus much interest in 'learning' BNs from data. In this paper the problem of learning a Bayesian network using integer programming is presented. The SCIP (Solving Constraint Integer Programming) framework is used to do this. Although cutting planes are a key ingredient in our approach, prima...

We propose a probabilistic framework to address uncertainty in ontology-based semantic integration and interoperation. This framework consists of three main components: 1) BayesOWL that translates an OWL ontology to a Bayesian network, 2) SLBN (Semantically Linked Bayesian Networks) that support reasoning across translated BNs, and 3) a Learner that learns from the web the probabilities needed ...

In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes. We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II Spirtes et al., 1993] and Causal MML Wallace and Korb, 1999]. These Bayesian nets are shown to signiica...

Effective knowledge integration plays a very important role in knowledge engineering and knowledgebased machine learning. The combination of Bayesian networks (BNs) has shown a promising technique in knowledge fusion and the way of combining BNs remains a challenging research topic. An effective method of BNs combination should not impose any particular constraints on the underlying BNs such th...

We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when restricted to instances that satisfy the following two conditions: they have bounded treewidth and the conditional probability table (CPT) at each node is specified concisely using an r-symmetric function for some constant r. Our polynomial time algorithms work directly on the unmoralized graph. Our...

Directed separation (d-separation) played a fundamental role in the founding of Bayesian networks (BNs) and continues to be useful today in a wide range of applications. Given an independence to be tested, current implementations of d-separation explore the active part of a BN. On the other hand, an overlooked property of d-separation implies that d-separation need only consider the relevant pa...

We consider the problem of learning Bayesian networks (BNs) from complete discrete data. This problem of discrete optimisation is formulated as an integer program (IP). We describe the various steps we have taken to allow efficient solving of this IP. These are (i) efficient search for cutting planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal) BNs and (iii) tighteni...

In this paper, we study the informationtheoretic 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 2 /m ) for non-sparse and sparse BNs respectively, where m is the n...

Bayesian Networks (BNs) have become one of the most powerful means of reconstructing signalling pathways in silico. Excessive computational loads limit the applications of BNs to learn larger sized network structures. Recent bioinformatics research found that signalling pathways are likely hierarchically organised. Genes resident in hierarchical layers constitute biological constraint, which ca...

We propose to compress Bayesian networks (BNs), reducing the space complexity to being fully linear, in order to widely deploy in low-resource platforms, such as smart mobile devices. We present a novel method that compresses each conditional probability table (CPT) of an arbitrary binary BN into a Non-impeding noisy-And Tree (NAT) model. It achieves the above goal in the binary case. Experimen...