Foundations of compositional models: inference

Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus the respective apparatus falls fully into probability theory. The present paper surveys the results supporting the design of computational procedures, without which the ap...

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Published in:International journal of general systems 2021-05, Vol.50 (4), p.409-433
Main Authors: Bína, Vl, Jiroušek, R., Kratochvíl, V.
Format: Article
Language:English
Subjects:
Bayesian analysis
Causality
conditioning
Inference
intervention
marginalization
multidimensionality
Probability distribution
Probability theory
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description Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus the respective apparatus falls fully into probability theory. The present paper surveys the results supporting the design of computational procedures, without which the application of these models to practical problems would be impossible. The methods of inference cannot do without a possibility to focus on a part of the considered multidimensional model and to incorporate data describing the actual situation. Thus the paper shows how to compute marginals and conditionals of multidimensional models. Also, the paper briefly solves the problem of computing the effect of an intervention, in case the model is interpreted as a causal model.
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subjects Bayesian analysis
Causality
conditioning
Inference
intervention
marginalization
multidimensionality
Probability distribution
Probability theory
title Foundations of compositional models: inference
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