Compositional models, Bayesian models and recursive factorization models

Francesco M. Malvestuto

Kybernetika (2016)

  • Volume: 52, Issue: 5, page 696-723
  • ISSN: 0023-5954

Abstract

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Compositional models are used to construct probability distributions from lower-order probability distributions. On the other hand, Bayesian models are used to represent probability distributions that factorize according to acyclic digraphs. We introduce a class of models, called recursive factorization models, to represent probability distributions that recursively factorize according to sequences of sets of variables, and prove that they have the same representation power as both compositional models generated by sequential expressions and Bayesian models. Moreover, we present a linear (graphical) algorithm for deciding if a conditional independence is valid in a given recursive factorization model.

How to cite

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Malvestuto, Francesco M.. "Compositional models, Bayesian models and recursive factorization models." Kybernetika 52.5 (2016): 696-723. <http://eudml.org/doc/287522>.

@article{Malvestuto2016,
abstract = {Compositional models are used to construct probability distributions from lower-order probability distributions. On the other hand, Bayesian models are used to represent probability distributions that factorize according to acyclic digraphs. We introduce a class of models, called recursive factorization models, to represent probability distributions that recursively factorize according to sequences of sets of variables, and prove that they have the same representation power as both compositional models generated by sequential expressions and Bayesian models. Moreover, we present a linear (graphical) algorithm for deciding if a conditional independence is valid in a given recursive factorization model.},
author = {Malvestuto, Francesco M.},
journal = {Kybernetika},
keywords = {Bayesian model; compositional model; conditional independence; Markov property; recursive model; sequential expression; Bayesian model; compositional model; conditional independence; Markov property; recursive model; sequential expression},
language = {eng},
number = {5},
pages = {696-723},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Compositional models, Bayesian models and recursive factorization models},
url = {http://eudml.org/doc/287522},
volume = {52},
year = {2016},
}

TY - JOUR
AU - Malvestuto, Francesco M.
TI - Compositional models, Bayesian models and recursive factorization models
JO - Kybernetika
PY - 2016
PB - Institute of Information Theory and Automation AS CR
VL - 52
IS - 5
SP - 696
EP - 723
AB - Compositional models are used to construct probability distributions from lower-order probability distributions. On the other hand, Bayesian models are used to represent probability distributions that factorize according to acyclic digraphs. We introduce a class of models, called recursive factorization models, to represent probability distributions that recursively factorize according to sequences of sets of variables, and prove that they have the same representation power as both compositional models generated by sequential expressions and Bayesian models. Moreover, we present a linear (graphical) algorithm for deciding if a conditional independence is valid in a given recursive factorization model.
LA - eng
KW - Bayesian model; compositional model; conditional independence; Markov property; recursive model; sequential expression; Bayesian model; compositional model; conditional independence; Markov property; recursive model; sequential expression
UR - http://eudml.org/doc/287522
ER -

References

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