Learning Probabilistic Graphical Models in R
Bellot, David
Sold by PBShop.store US, Wood Dale, IL, U.S.A.
AbeBooks Seller since April 7, 2005
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: Over 20 available
Add to basketSold by PBShop.store US, Wood Dale, IL, U.S.A.
AbeBooks Seller since April 7, 2005
Condition: New
Quantity: Over 20 available
Add to basketNew Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller Inventory # L0-9781784392055
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.
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, or 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.
"About this title" may belong to another edition of this title.
Returns Policy
We ask all customers to contact us for authorisation should they wish to return their order. Orders returned without authorisation may not be credited.
If you wish to return, please contact us within 14 days of receiving your order to obtain authorisation.
Returns requested beyond this time will not be authorised.
Our team will provide full instructions on how to return your order and once received our returns department will process your refund.
Please note the cost to return any...
Books are shipped from UK warehouse. Delivery thereafter is between 4 and 14 business days dependant upon your location - please do contact us with any queries you may have.
| Order quantity | 7 to 14 business days | 7 to 14 business days |
|---|---|---|
| First item | US$ 0.00 | US$ 0.00 |
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.