Reasoning with probabilistic and deterministic graphical models

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference.

Year

2013

Language

English

Series

Synthesis lectures on artificial intelligence and machine learning -- #23

Publisher

Morgan & Claypool Publishers

Editions

1

Pages

177

ISBN

9781627051989

Data source: Wikidata