資料來源: Google Book
Advanced mean field methods :theory and practice
- 其他作者: Opper, Manfred. , Saad, David.
- 出版: Cambridge, Mass. : MIT Press ©2001.
- 稽核項: 1 online resource (xiii, 273 pages) :illustrations.
- 叢書名: Neural information processing series
- 標題: COMPUTER SCIENCE/Machine Learning & Neural Networks , Atomic Physics. , Física matemática. , Mecânica estatística. , SCIENCE , SCIENCE Physics -- Mathematical & Computational. , Electronic books. , Fase-overgangen. , Neurale netwerken. , Mean field theory. , PhysicsMathematical & Computational. , Physical Sciences & Mathematics. , Physics. , Numerieke methoden. , Informatieverwerking (computer) , Mean-Field-Theorie , Aufsatzsammlung
- ISBN: 0262150549 , 9780262150545
- 試查全文@TNUA:
- 附註: Includes bibliographical references.
- 摘要: "Bringing together ideas and techniques from diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling."--Jacket.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=62618
- 系統號: 005298352
- 資料類型: 電子書
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- 引用網址: 複製連結
This book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling. A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.
來源: Google Book
來源: Google Book
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