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A Guided Tour of Artificial Intelligence ResearchBelief Graphical Models for Uncertainty Representation and Reasoning

A Guided Tour of Artificial Intelligence Research: Belief Graphical Models for Uncertainty... [Many real world problemsBenferhat, Salem and applications require to exploit incomplete, complex and uncertain information. BeliefLeray, Philippe graphical models encompass a wide range of graphical formalisms for representing and Reasoning reasoning with uncertain and complex information. They generallyTabia, Karim involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guided Tour of Artificial Intelligence ResearchBelief Graphical Models for Uncertainty Representation and Reasoning

Editors: Marquis, Pierre; Papini, Odile; Prade, Henri

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References (126)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-06166-1
Pages
209 –246
DOI
10.1007/978-3-030-06167-8_8
Publisher site
See Chapter on Publisher Site

Abstract

[Many real world problemsBenferhat, Salem and applications require to exploit incomplete, complex and uncertain information. BeliefLeray, Philippe graphical models encompass a wide range of graphical formalisms for representing and Reasoning reasoning with uncertain and complex information. They generallyTabia, Karim involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.]

Published: May 8, 2020

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