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Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot

(ebook) (audiobook) (audiobook) Język publikacji: angielski
Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot - okladka książki

Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot - okladka książki

Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot - audiobook MP3

Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot - audiobook CD

Autor:
David Bellot
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
250
Dostępne formaty:
     PDF
     ePub
     Mobi
Ebook
107,10 zł 119,00 zł (-10%)
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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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O autorze książki

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.

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