Gaussian Processes

Markov Processes, Gaussian Processes, and Local Times  

Posted by interes at April 13, 2015
Markov Processes, Gaussian Processes, and Local Times

Markov Processes, Gaussian Processes, and Local Times by Michael B. Marcus, Jay Rosen
English | 2006-07-24 | ISBN: 0521863007 | 630 pages | PDF | 3,2 Mb

Gaussian Processes for Machine Learning (repost)  

Posted by Veslefrikk at Oct. 21, 2014
Gaussian Processes for Machine Learning (repost)

Carl Edward Rasmussen, Christopher K. I. Williams, "Gaussian Processes for Machine Learning"
English | 2005-11-23 | ISBN: 026218253X | 266 pages | PDF | 11 mb

Lectures on Gaussian Processes [Repost]  

Posted by ChrisRedfield at Oct. 26, 2013
Lectures on Gaussian Processes [Repost]

Mikhail Lifshits - Lectures on Gaussian Processes
Published: 2012-01-13 | ISBN: 3642249388 | PDF | 120 pages | 3 MB

Gaussian Processes for Machine Learning (Repost)  

Posted by elodar at Sept. 9, 2013
Gaussian Processes for Machine Learning (Repost)

Carl Edward Rasmussen, Christopher K. I. Williams, "Gaussian Processes for Machine Learning"
English | 2005-11-23 | ISBN: 026218253X | 266 pages | PDF | 38.11 mb

Gaussian Processes for Machine Learning (repost)  

Posted by tot167 at Aug. 6, 2011
Gaussian Processes for Machine Learning (repost)

Carl Edward Rasmussen, Christopher K. I. Williams, "Gaussian Processes for Machine Learning"
The M,IT Press | 2005 | ISBN: 026218253X | 266 pages | PDF | 2,7 MB
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Publisher: The MIT Press | Author(s): Christopher K. I. Williams | ISBN:026218253X | Release Date: 01 December 2005 | 2.63 MB | Pages: 266 | deposifiles

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.

Random Processes by Example  

Posted by interes at April 4, 2014
Random Processes by Example

Random Processes by Example by Mikhail Lifshits
English | 2014 | ISBN: 9814522287 | ISBN-13: 9789814522281 | 230 pages | PDF | 1,6 MB

This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable distributions and processes.
An Introduction to Branching Measure-Valued Processes (repost)

An Introduction to Branching Measure-Valued Processes (Crm Monograph Series) by Eugene B. Dynkin
English | June 22, 1994 | ISBN: 0821802690 | Pages: 134 | DJVU | 1,2 MB

For about half a century, two classes of stochastic processes–Gaussian processes and processes with independent increments–have played an important role in the development of stochastic analysis and its applications. During the last decade, a third class–branching measure-valued (BMV) processes–has also been the subject of much research.
Upper and Lower Bounds for Stochastic Processes by Michel Talagrand [Repost]

Upper and Lower Bounds for Stochastic Processes: Modern Methods and Classical Problems by Michel Talagrand
English | 21 Feb. 2014 | ISBN: 3642540740 | 830 Pages | PDF | 6 MB

The book develops modern methods and in particular the "generic chaining" to bound stochastic processes. This methods allows in particular to get optimal bounds for Gaussian and Bernoulli processes.
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications by Vidyadhar S. Mandrekar and Leszek Gawarecki
English | 2015 | ISBN: 1498707815 | 201 pages | PDF | 2,5 MB