Bayesian Networks and Decision Graphs (Information Science and Statistics)

leondumoulin.nl: Bayesian Networks and Decision Graphs (Information Science and Statistics) (): Thomas Dyhre Nielsen, FINN VERNER.
Table of contents

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. Sponsored Products are advertisements for products sold by merchants on Amazon. When you click on a Sponsored Product ad, you will be taken to an Amazon detail page where you can learn more about the product and purchase it.

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Bayesian Networks and Decision Graphs

It is also very helpful for researchers in these fields and for those working in industry. All the illustrations are motivated by real applications. Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject.

Indian Journal of Statistics, Vol. It is well written, provides broad topic coverage, and is quite accessible to the non-expert. This would be an excellent edition to any personal library. It would make a very good text for a graduate or an advanced undergraduate course. Ghosh, International Statistical Reviews, Vol. Each chapter ends with a summary section, bibliographic notes, and exercises.

Its treatment is appropriate not just for statisticians, but also for computer scientists, engineers, and others researchers with appropriate mathematical background. It is useful as a reference for special topics. Lenz, Statistical Papers, Vol. Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty.


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As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.

The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The new edition is structured into two parts. The first part focuses on probabilistic graphical models.

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Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.

The book is intended as a textbook, but it can also be used for self-study and as a reference book. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support?

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Read more Read less. Prime Book Box for Kids. Customers who viewed this item also viewed. Page 1 of 1 Start over Page 1 of 1. Modeling and Reasoning with Bayesian Networks. Probabilistic Reasoning in Intelligent Systems: Bayesian Networks and Influence Diagrams: The Book of Why: The New Science of Cause and Effect. Customers who bought this item also bought.


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    Bayesian Networks and Decision Graphs

    Tor and the Deep Web: Master the dark art of anonymity today and get instant invisibility and access to thousands of deep web hidden websites. The darknet awaits you Review From the reviews: The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams and variants hereof , and Markov decision processes. Nielsen is an associate professor at the same department.

    Information Science and Statistics Hardcover: Springer; 2nd edition June 6, Language: Related Video Shorts 0 Upload your video. Try the Kindle edition and experience these great reading features: Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now. Please try again later. The language oftentimes is far from clear.

    Examples and explanations are not obvious at all. The author makes a lot of implicit assumptions. I had to get a few other books on Bayesian networks in order to understand author's explanations. One person found this helpful. I am very pleased to have found a book that gives a modern, sound, and self-contained introduction to Bayesian networks.

    The only prerequisite is basic knowledge of probability. This makes sense because a Bayesian network is essentially a directed graph whose vertex set is a collection of random variables, while an edge from one variable X to another variable Y represents a belief that X has a causative effect on Y. For example, X could be the pregnancy status of a cow, while Y could be a blood test administered to the cow. Bayesian Networks and Influence Diagrams: Support Vector Machines Ingo Steinwart.

    Information Theoretic Learning Jose C. Back cover copy Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering.

    This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models.

    The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network.

    Bayesian Networks and Decision Graphs

    The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams and variants hereof , and Markov decision processes.

    The book is intended as a textbook, but it can also be used for self-study and as a reference book. Jensen is a professor at the department of computer science at Aalborg University, Denmark.

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    Nielsen is an associate professor at the same department. A Practical Guide to Normative Systems: Algorithms for Normative Systems: Review Text 'From the reviews: It is also very helpful for researchers in these fields and for those working in industry. The book is self-contained The book has enough illustrative examples and exercises for the reader. All the illustrations are motivated by real applications.

    Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject. I certainly would not want to try to implement a BN application without reading this book. Indian Journal of Statistics, Vol. I think that the present volume represents a useful integration of other material and a compact guide for either a student who wants an introduction to the field or a teacher who needs a reference for a course on probabilistic reasoning in AI. I found this book to be an excellent introduction to the topic.

    It is well written, provides broad topic coverage, and is quite accessible to the non-expert. I think Bayesian Networks and Decision Graphs would make a fine text for an introductory class in Bayesian networks or a useful reference for anyone interested in learning about the field. This would be an excellent edition to any personal library. It would make a very good text for a graduate or an advanced undergraduate course. Altogether, this is a very useful book for anyone interested in learning Bayesian networks without tears.

    Ghosh, International Statistical Reviews, Vol. Each chapter ends with a summary section, bibliographic notes, and exercises. Its treatment is appropriate not just for statisticians, but also for computer scientists, engineers, and others researchers with appropriate mathematical background. It is useful as a reference for special topics. Review quote From the reviews: Lenz, Statistical Papers, Vol. Book ratings by Goodreads.