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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

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

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

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


Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书 用户评价

评分☆☆☆☆☆

learning from data, very informational.

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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learning from data, very informational.

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书 著者简介

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书 图书目录

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

Learning in Graphical Models (Adaptive Computation and Machine Learning) 在线电子书 图书描述

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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