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Modeling and Reasoning with Bayesian Networks

2009-04-06 Computers
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Release : 2009-04-06
Genre : Computers
Kind : eBook
Book Rating : 381/5 ( reviews)

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Book Synopsis Modeling and Reasoning with Bayesian Networks by : Adnan Darwiche

Download or read book Modeling and Reasoning with Bayesian Networks written by Adnan Darwiche. This book was released on 2009-04-06. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Modeling and Reasoning with Bayesian Networks

2009-04-06 Computers
Download Modeling and Reasoning with Bayesian Networks PDF Online Free

Author :
Release : 2009-04-06
Genre : Computers
Kind : eBook
Book Rating : 907/5 ( reviews)

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Book Synopsis Modeling and Reasoning with Bayesian Networks by : Adnan Darwiche

Download or read book Modeling and Reasoning with Bayesian Networks written by Adnan Darwiche. This book was released on 2009-04-06. Available in PDF, EPUB and Kindle. Book excerpt: This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Bayesian Networks and Decision Graphs

2009-03-17 Science
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Release : 2009-03-17
Genre : Science
Kind : eBook
Book Rating : 821/5 ( reviews)

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Book Synopsis Bayesian Networks and Decision Graphs by : Thomas Dyhre Nielsen

Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen. This book was released on 2009-03-17. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.

Modeling, Learning and Reasoning with Structured Bayesian Networks

2020
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Release : 2020
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Kind : eBook
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Book Synopsis Modeling, Learning and Reasoning with Structured Bayesian Networks by : Yujia Shen

Download or read book Modeling, Learning and Reasoning with Structured Bayesian Networks written by Yujia Shen. This book was released on 2020. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic graphical models, e.g. Bayesian Networks, have been traditionally introduced to model and reason with uncertainty. A graph structure is crafted to capture knowledge of conditional independence relationships among random variables, which can enhance the computational complexity of reasoning. To generate such a graph, one sometimes has to provide vast and detailed knowledge about how variables interacts, which may not be readily available. In some cases, although a graph structure can be obtained from available knowledge, it can be too dense to be useful computationally. In this dissertation, we propose a new type of probabilistic graphical models called a Structured Bayesian network (SBN) that requires less detailed knowledge about conditional independences. The new model can also leverage other types of knowledge, including logical constraints and conditional independencies that are not visible in the graph structure. Using SBNs, different types of knowledge act in harmony to facilitate reasoning and learning from a stochastic world. We study SBNs across the dimensions of modeling, inference and learning. We also demonstrate some of their applications in the domain of traffic modeling.

Probabilistic Reasoning in Multiagent Systems

2002-08-26 Computers
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Release : 2002-08-26
Genre : Computers
Kind : eBook
Book Rating : 462/5 ( reviews)

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Book Synopsis Probabilistic Reasoning in Multiagent Systems by : Yang Xiang

Download or read book Probabilistic Reasoning in Multiagent Systems written by Yang Xiang. This book was released on 2002-08-26. Available in PDF, EPUB and Kindle. Book excerpt: This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

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