Book ChapterDOI
On the problem of performing exact partial abductive inference in Bayesian belief networks using junction trees
Luis M. de Campos,José A. Gámez,Serafín Moral +2 more
- pp 289-302
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TLDR
The experimental results reveal that the problem of partial abductive inference is difficult to solve by exact computation.Abstract:
Partial abductive inference in Bayesian belief networks has been usually expressed as an extension of total abductive inference (abduction over all the variables in the network). In this paper we study the transformation of the partial problem in a total one, analyzing and trying to improve the method previously appeared in the literature. We also outline an alternative approach, and compare both methods by means of experimentation. The experimental results reveal that the problem of partial abductive inference is difficult to solve by exact computation.read more
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Journal ArticleDOI
Probabilistic graphical models in artificial intelligence
Pedro Larrañaga,Serafín Moral +1 more
TL;DR: The role of probabilistic graphical models in artificial intelligence is reviewed and the main milestones for the foundations of graphical models starting with Pearl's pioneering work are discussed.
BookDOI
Innovations in Bayesian Networks
Dawn E. Holmes,Lakhmi C. Jain +1 more
TL;DR: Innovations in Bayesian network, Innovations inBayesian network , کتابخانه دیجیتال جندی شاپور اهواز
Journal ArticleDOI
Searching for the best elimination sequence in Bayesian networks by using ant colony optimization
José A. Gámez,José M. Puerta +1 more
TL;DR: This paper investigates the applicability to this problem of a new combinatorial optimization technique, inspired by a natural model, which has appeared recently: ant colony optimization, and is validated by using a set of complex networks obtained from a repository.
Journal ArticleDOI
Partial abductive inference in Bayesian belief networks - an evolutionary computation approach by using problem-specific genetic operators
TL;DR: A genetic algorithm is used to perform partial abductive inference in Bayesian belief networks and it is concluded that the new genetic operators preserve the accuracy of the previous algorithm and also reduce the number of operations performed during the evaluation of individuals.
Book ChapterDOI
Abductive Inference in Bayesian Networks: A Review
TL;DR: The goal of this paper is to serve as a survey for the problem of abductive inference (or belief revision) in Bayesian networks by introducing the problem in its two variants: total abduction and partial abduction.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Journal ArticleDOI
Local computations with probabilities on graphical structures and their application to expert systems
Book
An introduction to Bayesian networks
TL;DR: The principal ideas of probabilistic reasoning - known as Bayesian networks - are outlined and their practical implications illustrated and are intended for MSc students in knowledge-based systems, artificial intelligence and statistics, and for professionals in decision support systems applications and research.
Book
Probabilistic Reasoning In Expert Systems: Theory and Algorithms
TL;DR: This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field the authors now call Bayesian networks and provides an insightful comparison of the two most prominent approaches to probability.