Read An Introduction to Lifted Probabilistic Inference (Neural Information Processing series)

An Introduction to Lifted Probabilistic Inference (Neural Information Processing series)

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Description:Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models.Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with An Introduction to Lifted Probabilistic Inference (Neural Information Processing series). To get started finding An Introduction to Lifted Probabilistic Inference (Neural Information Processing series), you are right to find our website which has a comprehensive collection of manuals listed.
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ISBN: 0262542595

Description: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models.Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with An Introduction to Lifted Probabilistic Inference (Neural Information Processing series). To get started finding An Introduction to Lifted Probabilistic Inference (Neural Information Processing series), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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An Introduction to Lifted Probabilistic Inference (Neural Information Processing series)

An Introduction to Lifted Probabilistic Inference (Neural Information Processing series)

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