Reasoning with probabilistic and deterministic graphical models - Info and Reading Options
exact algorithms
By Rina Dechter
"Reasoning with probabilistic and deterministic graphical models" was published by Morgan & Claypool Publishers in 2013 - San Rafael, California], it has 177 pages and the language of the book is English.
“Reasoning with probabilistic and deterministic graphical models” Metadata:
- Title: ➤ Reasoning with probabilistic and deterministic graphical models
- Author: Rina Dechter
- Language: English
- Number of Pages: 177
- Publisher: Morgan & Claypool Publishers
- Publish Date: 2013
- Publish Location: San Rafael, California]
“Reasoning with probabilistic and deterministic graphical models” Subjects and Themes:
- Subjects: ➤ Graphical modeling (Statistics) - Bayesian statistical decision theory - Reasoning - Algorithms - Machine learning - COMPUTERS - General - Technology - Computers
Edition Specifications:
- Pagination: ➤ 1 online resource (xiv, 177 pages)
Edition Identifiers:
- The Open Library ID: OL32966768M - OL24809337W
- Online Computer Library Center (OCLC) ID: 867318552
- ISBN-13: 9781627051989 - 9781627051972
- ISBN-10: 1627051988 - 162705197X
- All ISBNs: 1627051988 - 162705197X - 9781627051989 - 9781627051972
AI-generated Review of “Reasoning with probabilistic and deterministic graphical models”:
"Reasoning with probabilistic and deterministic graphical models" Table Of Contents:
- 1- What are graphical models
- 2- Inference : bucket elimination for deterministic networks
- 3- Inference : bucket elimination for probabilistic networks
- 4- Tree-clustering schemes
- 5- AND/OR search spaces and algorithms for graphical models
- 6- Combining search and inference : trading space for time.
"Reasoning with probabilistic and deterministic graphical models" Description:
The Open Library:
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.
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