In Libra, each probabilistic model represents a probability distribution, P (X ), over set of discrete random variables, X = {X1, X2, . . . , Xn}. Libra supports Bayesian networks (BNs), Markov networks (MNs), dependency networks (DNs) [8], sumproduct networks (SPNs) [19], arithmetic circuits (ACs) [6], and mixtures of trees (MT) [17]. BNs and DNs represent a probability distribution as a colle...

In Libra, each probabilistic model represents a probability distribution, P (X ), over set of discrete random variables, X = {X1, X2, . . . , Xn}. Libra supports Bayesian networks (BNs), Markov networks (MNs), dependency networks (DNs) [8], sumproduct networks (SPNs) [19], arithmetic circuits (ACs) [6], and mixtures of trees (MT) [17]. BNs and DNs represent a probability distribution as a colle...

Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficien...

In this paper, we propose a unified approach to VB (variational Bayes) [1] in symbolicstatistical modeling via propositionalization1. By propositionalization we mean, broadly, expressing and computing probabilistic models such as BNs (Bayesian networks) [2] and PCFGs (probabilistic context free grammars) [3] in terms of propositional logic that considers propositional variables as binary random...

This paper is about helping people who make critical decisions improve the quality of their judgements. We provide a brief introduction to Bayesian Nets (BNs) and use an example in safety assessment. We show how BNs enable decision-makers to combine different types of evidence (including subjective judgements) to provide quantitative, auditable arguments. By using state-ofthe-art BN technology ...

This paper deals with the problem of searching for the best assignments of random variables to nodes in a Bayesian network (BN) with a given topology. Likelihood functions for the studied BNs are formulated, methods for their maximization are described and, finally, the results of a study concerning the reliability of revealing BNs’ roles are reported. The results of BN node assignments can be ...

OBJECTIVE we set the threshold to achieve a sensitivity of 95% To compare a Bayesian network (BN) and an and then calculated the specificity, the positive artificial neural network (ANN) in diagnosing predictive value (PPV), and the negative predictive community-acquired pneumonia. value (NPV). Over the range of all thresholds, we also plotted sensitivity against 1 specificity to get BACKGROUND...

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on physical, chemical biological processes and, thus, modeling variations associated with stormy precipitation episodes is essential, especially since this kind becoming increasingly frequ...

Bayesian networks (BNs) are used increasingly to model environmental systems, for reasons including their ability to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty. For a model to be of value in generating and sharing knowledge or providing decision support, it must be built using good modelling practice. Thi...

This research is motivated by the need t o support inference across multiple intelligence systems involving uncertainty. Our objective is to develop a theoretical framework and related inference methods to map semantically similar variables between separate Bayesian networks in a principled way. T he work is to be conducted in two steps. In the first step, we investigate the problem of formaliz...