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This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t.
Book Synopsis Approximation Methods for Efficient Learning of Bayesian Networks by : Carsten Riggelsen
Download or read book Approximation Methods for Efficient Learning of Bayesian Networks written by Carsten Riggelsen and published by IOS Press. This book was released on 2008 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t.
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.
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 and published by Cambridge University Press. This book was released on 2009-04-06 with total page 561 pages. 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.
The primary objective of the research is to develop and validate the approach and algorithms for efficiently learning a complex web of relations from a combination of prior knowledge, published literature, and high-dimensional data. Algorithms for inferring the structure of Bayesian networks (BNs) from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian model averaging method, Markov Chain Monte Carlo (MCMC), is typically applied for BN structural learning from data. However, existing state-of-the-art MCMC-based learning algorithms are rather slow in mixing and convergence in high-dimensional domains. To address these challenges, we first developed and tested intelligent strategies for prioritizing the structural search space using prior information. Second, we present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the entire Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance when the number of observations is relatively small and faster convergence when the number of variables in the network is large. It is anticipated that our methodology will be especially useful for deciphering how genes and the environment interact to determine cancer risk by allowing BNs to be extended to a genome-wide scale.
Book Synopsis A Novel Algorithm for Efficient Learning of Bayesian Networks from High-dimensional Data and Prior Knowledge by : Chengwei Su
Download or read book A Novel Algorithm for Efficient Learning of Bayesian Networks from High-dimensional Data and Prior Knowledge written by Chengwei Su and published by . This book was released on 2014 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of the research is to develop and validate the approach and algorithms for efficiently learning a complex web of relations from a combination of prior knowledge, published literature, and high-dimensional data. Algorithms for inferring the structure of Bayesian networks (BNs) from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian model averaging method, Markov Chain Monte Carlo (MCMC), is typically applied for BN structural learning from data. However, existing state-of-the-art MCMC-based learning algorithms are rather slow in mixing and convergence in high-dimensional domains. To address these challenges, we first developed and tested intelligent strategies for prioritizing the structural search space using prior information. Second, we present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the entire Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance when the number of observations is relatively small and faster convergence when the number of variables in the network is large. It is anticipated that our methodology will be especially useful for deciphering how genes and the environment interact to determine cancer risk by allowing BNs to be extended to a genome-wide scale.
Download or read book written by and published by IOS Press. This book was released on with total page 4947 pages. Available in PDF, EPUB and Kindle. Book excerpt:
"This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
Book Synopsis Handbook of Research on Computational Methodologies in Gene Regulatory Networks by : Das, Sanjoy
Download or read book Handbook of Research on Computational Methodologies in Gene Regulatory Networks written by Das, Sanjoy and published by IGI Global. This book was released on 2009-10-31 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Book Synopsis Learning Bayesian Networks by : Richard E. Neapolitan
Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
Book Synopsis Innovations in Bayesian Networks by : Dawn E. Holmes
Download or read book Innovations in Bayesian Networks written by Dawn E. Holmes and published by Springer Science & Business Media. This book was released on 2008-10-02 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
Book Synopsis Insights in Reinforcement Learning by : Hado Philip van Hasselt
Download or read book Insights in Reinforcement Learning written by Hado Philip van Hasselt and published by Hado van Hasselt. This book was released on 2011 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Legal Theory, Sources of Law and the Semantic Web is an attempt to construct an integrated conceptual framework for the application-neutral and problem-neutral representation of sources of law using Semantic Web technology and concepts, and some technically straightforward extensions to Semantic Web technology based on established practices found in fielded applications. To construct this framework, the author disentangled some problems that are often mixed up in legal theory and – in extension – legal knowledge representation. The purpose of this framework is to provide a theoretical background for the creation of reusable and maintainable knowledge components representing knowledge of sources of law on the Semantic Web. These components should form a basis for the development for computer applications supporting straightforward, routine decision making problems using traditional methods. This book aims to be a work of ontology: an account of relevant aspects of the knowledge domain of law from the perspective of a legal knowledge engineer interested in sources of law. One cannot however say that the result of this work is an ontology: this book presents a mix of design principles, design patterns for knowledge representation in OWL DL and ontology fragments.
Book Synopsis Legal Theory, Sources of Law and the Semantic Web by : A. Boer
Download or read book Legal Theory, Sources of Law and the Semantic Web written by A. Boer and published by IOS Press. This book was released on 2009-05-13 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Legal Theory, Sources of Law and the Semantic Web is an attempt to construct an integrated conceptual framework for the application-neutral and problem-neutral representation of sources of law using Semantic Web technology and concepts, and some technically straightforward extensions to Semantic Web technology based on established practices found in fielded applications. To construct this framework, the author disentangled some problems that are often mixed up in legal theory and – in extension – legal knowledge representation. The purpose of this framework is to provide a theoretical background for the creation of reusable and maintainable knowledge components representing knowledge of sources of law on the Semantic Web. These components should form a basis for the development for computer applications supporting straightforward, routine decision making problems using traditional methods. This book aims to be a work of ontology: an account of relevant aspects of the knowledge domain of law from the perspective of a legal knowledge engineer interested in sources of law. One cannot however say that the result of this work is an ontology: this book presents a mix of design principles, design patterns for knowledge representation in OWL DL and ontology fragments.
Book Synopsis Designing controls for network organizations by : Vera Kartseva
Download or read book Designing controls for network organizations written by Vera Kartseva and published by Rozenberg Publishers. This book was released on 2008 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:
