Probabilistic Graphical Models 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Daphne Koller 作者

The MIT Press

译者

2009-7-31 出版日期

1280 页数

USD 120.00 价格

Hardcover

Adaptive Computation and Machine Learning 丛书系列

9780262013192 图书编码

Probabilistic Graphical Models 在线电子书图书标签: 机器学习概率图模型 Graph-Model 数学 MachineLearning 计算机数据挖掘算法

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Probabilistic Graphical Models 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Probabilistic Graphical Models 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Probabilistic Graphical Models 在线电子书用户评价

评分☆☆☆☆☆

渣就一个字。废话太多，又不cover领域前沿。讲的都是没用的，好东西没讲到。不如直接看Martin Wainwright, Michael Joradn的review论文

评分☆☆☆☆☆

看看还是觉得挺喜欢的，很成体系。唯一的缺点是太抽象了。再者，里面的错误也是不少的，包括一些排版错误和一些公式推导的错误。但是，书内容很全，也算是很新吧。

评分☆☆☆☆☆

垃圾！啰嗦，定义不清晰，redundant。

评分☆☆☆☆☆

渣就一个字。废话太多，又不cover领域前沿。讲的都是没用的，好东西没讲到。不如直接看Martin Wainwright, Michael Joradn的review论文

评分☆☆☆☆☆

深入浅出。可惜出得太晚了，我花了三四年时间读了无数的书，tutorial，论文，总算把相关内容搞清楚了。如果以前有这本书，可能只读这一本就好了。

Probabilistic Graphical Models 在线电子书著者简介

Probabilistic Graphical Models 在线电子书图书目录

Probabilistic Graphical Models 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Probabilistic Graphical Models 在线电子书图书描述

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Probabilistic Graphical Models 在线电子书下载 mobi epub pdf txt 在线电子书下载

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