We propose a regime aware learning algorithm to learn a sequence of Bayesian networks (BNs) that model a system that undergoes regime changes. The last BN in the sequence represents the system’s current regime, and should be used for BN inference. To explore the feasibility of the algorithm, we create baseline tests against learning a singe BN, and show that our proposed algorithm outperforms t...

We propose a regime aware learning algorithm to learn a sequence of Bayesian networks (BNs) that model a system that undergoes regime changes. The last BN in the sequence represents the system’s current regime, and should be used for BN inference. To explore the feasibility of the algorithm, we create baseline tests against learning a singe BN, and show that our proposed algorithm outperforms t...

Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially when training data are hard to acquire. Two approaches have been used to address this challenge: 1) introducing expert judgements and 2) transferring knowledge from related domains. This is the first paper to present a generic framework that combines both approaches to improve BN parameter learni...

Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this paper, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem; named BNC-PSO...

MOTIVATION An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different ...

Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability. The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle length and number of axles. Because of the nature of traffic configuration, the quantities measured are evidently regarded as random variables. The dependence structure of the data of such compl...

We propose statistical abduction as a rstorder logical framework for representing and learning probabilistic knowledge. It combines logical abduction with a parameterized distribution over abducibles. We show that probability computation, a Viterbilike algorithm and EM learning for statistical abduction achieve the same eÆciency as specilzed algorithms for HMMs (hidden Markov models), PCFGs (pr...

A large amount of work has been done in the last ten years on learning parameters and structure in Bayesian networks (BNs) (see for example Neapolitan, 2005). Within the classical Bayesian framework, learning parameters in BNs is based on priors; a prior distribution of the parameters (prior conditional probabilities) is chosen and a posterior distribution is then derived given the data and pri...

Bayesian networks (BNs) can capture interdependencies among ontology mapping methods and thus possibly improve the way they are combined. Experiments on ontologies from the OAEI collection are shown, and the possibility of modelling explicit mapping patterns in combination with methods is discussed.

In this paper an approach for coupling real-time control and socio-economic issues in participatory river basin planning is presented through a case study. It relies on the use of Bayesian Networks (Bns) to describe in a probabilistic way the behaviour of farmers within an irrigation district in response to some planning actions. Bayesian Networks are coupled with classical stochastic hydrologi...