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Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Learning in Graphical Models (Adaptive Computation and Machine Learning)
☆☆☆☆☆
簡體網頁||繁體網頁
Jordan, Michael I. 編 作者
The MIT Press
譯者
1998-11-27 出版日期
644 頁數
USD 75.00 價格
Paperback
Adaptive Computation and Machine Learning 叢書系列
9780262600323 圖書編碼

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 圖書標籤: 機器學習  Graph-Model  圖模型  learning  Graphical  美國  統計學  機器學習   

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Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 用戶評價

評分☆☆☆☆☆

本來可以個四星的，不過近年來有很多體係完善的相關圖書齣現，這本論文集式的圖書價值多少有點打摺。

評分☆☆☆☆☆

本來可以個四星的，不過近年來有很多體係完善的相關圖書齣現，這本論文集式的圖書價值多少有點打摺。

評分☆☆☆☆☆

learning from data, very informational.

評分☆☆☆☆☆

learning from data, very informational.

評分☆☆☆☆☆

本來可以個四星的，不過近年來有很多體係完善的相關圖書齣現，這本論文集式的圖書價值多少有點打摺。

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 著者簡介

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 著者簡介

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在線電子書 圖書描述

Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.

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