This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.

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A Bayesian Method for the Induction of Probabilistic Networks from Data

Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning.

Machine Learning: Neural and Statistical Classification

Pendant plus d'un demi-siecle, les graphes ont ete utilises pour representer des modeles statistiques en analyse de donnees. En particulier, les graphes decrivant l'independance conditionnelle sont apparus comme une classe importante de ces modeles. Des applications en analyse d'image, analyse de pedigree, ou encore en systeme expert sont a l'origine de leur developpement, et plusieurs livres de synthese ont deja ete publies a ce sujet. Le developpement d'un cadre Bayesien de ces modeles est en revanche moins connu, et les applications en systemes experts ont motive la recherche dans ce domaine. La possibilite de construire des modeles capables de se remettre a jour au fur et a mesure que de nouvelles donnees sont disponibles est a l'origine d'un interet intense de la part de la communaute travaillant en intelligence artificielle. Cependent, leur application a une classe plus vaste d'analyse de donnees a ete largement negligee. L'objet de cet article est de montrer comment les modeles Bayesiens de graphes permettent d'unifier et de simplifier des problemes standards, tels que les modeles log-lineaires Bayesiens (avec des donnees completes ou non), l'estimation d'une population fermee ou le double echantillonnage. Dans la mesure ou le choix d'un modele conventionel unique echoue dans ce type de situation, nous construisons des distributions a posteriori des quantites d'interet en moyennant sur les modeles possibles. Plus particulierement, nous introduisons la composition de chaines de Markov-Monte Carlo, une methode de Monte-Carlo permettant de moyenner sur les modeles retenus.

https://www.jstor.org/stable/pdfplus/1403615.pdf

Bayesian Graphical Models for Discrete Data

Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing with this uncertainty using an underlying graphical structure and the probability calculus. Bayesian networks have been successfully implemented in areas as diverse as medical diagnosis and finance. We present a brief introduction to Bayesian networks for those readers new to them and give some pointers to the literature.

Introduction to Bayesian Networks

Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application Software.

Richard E. Neapolitan

Papers

Probabilistic reasoning in expert systems