Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R - Helion
Tytuł oryginału: Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R
ISBN: 9781784397418
stron: 250, Format: ebook
Data wydania: 2016-04-29
Księgarnia: Helion
Cena książki: 109,00 zł
Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.
We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction.
Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.
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Spis treści
Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R eBook -- spis treści
- 1. Probabilistic Reasoning
- 2. Exact Inference
- 3. Learning parameters
- 4. Bayesian modeling : Basic models
- 5. Approximate inference
- 6. Bayesian modeling: Linear models
- 7. Probabilistic Mixture Models
- 8. Appendix