Open AccessProbabilistic graphical models
Author(s) -
Alessandro Antonucci,
Cassio P. de Campos,
Marco Zaffalon
Publication year - 2014
Publication title -
wiley series in probability and statistics
Language(s) - English
Resource type - Book series
eISSN - 1940-6347
pISSN - 1940-6517
DOI - 10.1002/9781118763117.ch9
Subject(s) - graphical model , probabilistic logic , generalization , bayesian network , class (philosophy) , computer science , independence (probability theory) , directed acyclic graph , probabilistic relevance model , set (abstract data type) , conditional independence , statistical model , theoretical computer science , artificial intelligence , mathematics , algorithm , probabilistic analysis of algorithms , programming language , statistics , mathematical analysis
This report presents probabilistic graphical models that are based on imprecise probabilities using a comprehensive language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform inference, and discusses on complexity results and related work. The goal is to present an easy-to-follow introduction to the topic.
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