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Learning Probabilistic Graphical Models in R
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Index
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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