Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Jordan, Michael I. 编作者

The MIT Press

译者

1998-11-27 出版日期

644 页数

USD 75.00 价格

Paperback

Adaptive Computation and Machine Learning 丛书系列

9780262600323 图书编码

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书图书标签: 机器学习 Graph-Model 图模型 learning Graphical 美國统计学機器學習

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发表于2024-11-22

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评分☆☆☆☆☆

learning from data, very informational.

评分☆☆☆☆☆

learning from data, very informational.

评分☆☆☆☆☆

learning from data, very informational.

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本来可以个四星的，不过近年来有很多体系完善的相关图书出现，这本论文集式的图书价值多少有点打折。

评分☆☆☆☆☆

learning from data, very informational.

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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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