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Learning Probabilistic Graphical Models in R

更新時間：2021-07-16 11:02:58

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Thisbookisforanyonewhohastodealwithlotsofdataanddrawconclusionsfromit,especiallywhenthedataisnoisyoruncertain.Datascientists,machinelearningenthusiasts,engineers,andthosewhocuriousaboutthelatestadvancesinmachinelearningwillfindPGMinteresting.

目錄(60章)

倒序

coverpage
Learning Probabilistic Graphical Models in R
Credits
About the Author
About the Reviewers
www.PacktPub.com
eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Probabilistic Reasoning
Machine learning
Representing uncertainty with probabilities
Probabilistic graphical models
Summary
Chapter 2. Exact Inference
Building graphical models
Variable elimination
Sum-product and belief updates
The junction tree algorithm
Examples of probabilistic graphical models
Summary
Chapter 3. Learning Parameters
Introduction
Learning by inference
Maximum likelihood
Learning with hidden variables – the EM algorithm
Principles of the EM algorithm
Summary
Chapter 4. Bayesian Modeling – Basic Models
The Naive Bayes model
Beta-Binomial
The Gaussian mixture model
Summary
Chapter 5. Approximate Inference
Sampling from a distribution
Basic sampling algorithms
Rejection sampling
Importance sampling
Markov Chain Monte-Carlo
MCMC for probabilistic graphical models in R
Summary
Chapter 6. Bayesian Modeling – Linear Models
Linear regression
Bayesian linear models
Summary
Chapter 7. Probabilistic Mixture Models
Mixture models
EM for mixture models
Mixture of Bernoulli
Mixture of experts
Latent Dirichlet Allocation
Summary
Appendix A. Appendix
References
Index 更新時間：2021-07-16 11:02:58

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Learning Probabilistic Graphical Models in R