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

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Jordan, Michael I. 編

The MIT Press

1998-11-27

644

USD 75.00

Paperback

Adaptive Computation and Machine Learning

9780262600323

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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发表于2024-12-24

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

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

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.

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