Posted by **AvaxGenius** at June 4, 2018

English | PDF,EPUB | 2018 | 277 Pages | ISBN : 3319555677 | 12.31 MB

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on.

Posted by **exLib** at May 17, 2018

BB / MIT Press | 2002 | ISBN: 0585437122 9780585437125 | 335 pages | PDF | 3 MB

The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.

Posted by **insetes** at Oct. 13, 2017

2015 | 168 Pages | ISBN: 178398760X | EPUB | 3 MB

Posted by **readerXXI** at Oct. 6, 2017

English | 2015 | ISBN: 178398760X | 168 Pages | Mobi+Code Files | 6.5 MB

Posted by **readerXXI** at April 10, 2017

English | 2015 | ISBN: 178398760X | 165 Pages | True PDF | 1.48 MB

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems.

Posted by **nebulae** at Dec. 14, 2016

English | ISBN: 1574446134 | 2006 | 304 pages | PDF | 4 MB

Posted by **enmoys** at May 26, 2016

2015 | 168 Pages | ISBN: 178398760X | EPUB | 3 MB

Posted by **Underaglassmoon** at May 24, 2016

Springer | Statistical Theory and Methods | June 21, 2016 | ISBN-10: 9811008884 | 529 pages | pdf | 3.97 mb

Authors: Dixit, Ulhas Jayram

Presents sophisticated mathematical proofs in a simple and easy-to-follow language

Discusses fundamental topics common to many fields of statistical inference, and which offer a point of departure for in-depth study

Posted by **DZ123** at Nov. 2, 2015

English | 1992 | ISBN: 0262050463 | DJVU | pages: 287 | 1,9 mb

Posted by **AlenMiler** at Oct. 30, 2015

English | 28 Oct. 2015 | ISBN: 178398760X | 168 Pages | AZW3 (Kindle)/HTML/EPUB/PDF (conv) | 19 MB

This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications.