Possibilistic Graphical Models for Uncertainty Modeling

Tabia, Karim

doi:10.1007/978-3-319-45856-4_3

Cited by 2 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Learning PNs from data amounts to derive the structure and the local possibility tables of each variable from a dataset. Learning PNs makes sense within quantitative interpretations of possibility distributions and it is suitable especially in case of learning with imprecise data, scarce datasets and learning from datasets with missing values [Tabia, 2016]. Similar to learning the structure of Bayesian networks, two main approaches are used for possibilistic networks structure learning: i) Constraint-based methods where the principle is to detect conditional independence relations I by performing a set of tests on the training dataset then try to find a DAG that satisfies I seen as a set of constraints.…”

Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%

Belief Graphical Models for Uncertainty Representation and Reasoning

Benferhat

Leray

Tabia

2020

A Guided Tour of Artificial Intelligence Research

Self Cite

View full text Add to dashboard Cite

Many real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally 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.

show abstract

Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%