Optimized realization of Bayesian networks in reduced normal form using latent variable model

Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures...

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Vydané v:Soft computing (Berlin, Germany) Ročník 25; číslo 10; s. 7029 - 7040
Hlavní autori: Gennaro, Giovanni Di, Buonanno, Amedeo, Palmieri, Francesco A. N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Bayesian analysis
Canonical forms
Computational Intelligence
Control
Cost analysis
Engineering
Graphs
Libraries
Machine learning
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Networks
Probability
Robotics
Structural analysis
Variables
Latent variable
Factor graphs
Bayesian networks
Optimization
Belief propagation
ISSN:1432-7643, 1433-7479
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Popis
Shrnutí:Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-05642-3