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
- Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package
This book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.
What You Will Learn
- Understand the concepts of PGM and which type of PGM to use for which problem
- Tune the model's parameters and explore new models automatically
- Understand the basic principles of Bayesian models, from simple to advanced
- Transform the old linear regression model into a powerful probabilistic model
- Use standard industry models but with the power of PGM
- Understand the advanced models used throughout today's industry
- See how to compute posterior distribution with exact and approximate inference algorithms
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.
Style and approach
This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.
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About the Author
David Bellot is a PhD graduate in computer science from INRIA, France, with a focus on Bayesian machine learning. He was a postdoctoral fellow at the University of California, Berkeley, and worked for companies such as Intel, Orange, and Barclays Bank. He currently works in the financial industry, where he develops financial market prediction algorithms using machine learning. He is also a contributor to open source projects such as the Boost C++ library.