Learning probabilistic graphical models in R familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R
Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book Predict and use a probabilistic graphical models (PGM) as an expert system Comprehend how your computer can learn Bayesian modeling to solve real-world problems Kn...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2016.
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| Edición: | 1st edition |
| Colección: | Community experience distilled.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630274506719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Table of Contents
- Preface
- Chapter 1: Probabilistic Reasoning
- Machine learning
- Representing uncertainty with probabilities
- Beliefs and uncertainty as probabilities
- Conditional probability
- Probability calculus and random variables
- Sample space, events, and probability
- Random variables and probability calculus
- Joint probability distributions
- Bayes' rule
- Interpreting the Bayes' formula
- A first example of Bayes' rule
- A first example of Bayes' rule in R
- Probabilistic graphical models
- Probabilistic models
- Graphs and conditional independence
- Factorizing a distribution
- Directed models
- Undirected models
- Examples and applications
- Summary
- Chapter 2: Exact Inference
- Building graphical models
- Types of random variable
- Building graphs
- Probabilistic expert system
- Basic structures in probabilistic graphical models
- Variable elimination
- Sum-product and belief updates
- The junction tree algorithm
- Examples of probabilistic graphical models
- The sprinkler example
- The medical expert system
- Models with more than two layers
- Tree structure
- Summary
- Chapter 3: Learning Parameters
- Introduction
- Learning by inference
- Maximum likelihood
- How are empirical and model distribution related?
- The ML algorithm and its implementation in R
- Application
- Learning with hidden variables - the EM algorithm
- Latent variables
- Principles of the EM algorithm
- Derivation of the EM algorithm
- Applying EM to graphical models
- Summary
- Chapter 4: Bayesian Modeling - Basic Models
- The Naive Bayes model
- Representation
- Learning the Naive Bayes model
- Bayesian Naive Bayes
- Beta-Binomial
- The prior distribution.
- The posterior distribution with the conjugacy property
- Which values should we choose for the Beta parameters?
- The Gaussian mixture model
- Definition
- Summary
- Chapter 5: Approximate Inference
- Sampling from a distribution
- Basic sampling algorithms
- Standard distributions
- Rejection sampling
- An implementation in R
- Importance sampling
- An implementation in R
- Markov Chain Monte-Carlo
- General idea of the method
- The Metropolis-Hastings algorithm
- MCMC for probabilistic graphical models in R
- Installing Stan and RStan
- A simple example in RStan
- Summary
- Chapter 6: Bayesian Modeling - Linear Models
- Linear regression
- Estimating the parameters
- Bayesian linear models
- Over-fitting a model
- Graphical model of a linear model
- Posterior distribution
- Implementation in R
- A stable implementation
- More packages in R
- Summary
- Chapter 7: Probabilistic Mixture Models
- Mixture models
- EM for mixture models
- Mixture of Bernoulli
- Mixture of experts
- Latent Dirichlet Allocation
- The LDA model
- Variational inference
- Examples
- Summary
- Appendix
- References
- Books on the Bayesian theory
- Books on machine learning
- Papers
- Index.
