Posted by **nebulae** at March 14, 2017

English | ISBN: 1118959019 | 2017 | 352 pages | PDF | 8 MB

Posted by **Underaglassmoon** at Jan. 13, 2017

Princeton | English | 2016 | ISBN-10: 0691161089 | 296 pages | PDF | 7.18 mb

by Edward P. Herbst (Author), Frank Schorfheide (Author)

Posted by **Underaglassmoon** at Aug. 1, 2016

Springer | Statistics | August 28, 2016 | ISBN-10: 3319327887 | 327 pages | pdf | 3.14 mb

Authors: Phadia, Eswar G.

Provides valuable resource for nonparametric Bayesian analysis of big data

Includes a section on machine learning

Shows practical examples

Posted by **step778** at Jan. 23, 2016

2008 | pages: 204 | ISBN: 3540786562 | PDF | 7,4 mb

Posted by **ChrisRedfield** at Dec. 2, 2015

Published: 2013-07-25 | ISBN: 3642392792, 3642429319 | PDF | 207 pages | 2.21 MB

Posted by **Rare-1** at July 14, 2015

English | Springer (2008) | ISBN-10: 3540786562 | 206 pages | ُPDF | 7.40 MB

This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.

Posted by **interes** at Aug. 19, 2014

English | ISBN: 0470621702 | 2012 | 400 pages | PDF | 13,5 MB

A practical approach to estimating and tracking dynamic systems in real–worl applications Much of the literature on performing estimation for non–Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation.

Posted by **step778** at June 11, 2014

2008 | pages: 203 | ISBN: 3540786562 | PDF | 7,4 mb

Posted by **zolao** at July 17, 2013

English | ISBN: 0470621702 | 2012 | 400 pages | PDF | 14 MB

A practical approach to estimating and tracking dynamic systems in real–worl applications Much of the literature on performing estimation for non–Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation.

Posted by **roxul** at July 4, 2013

English | ISBN: 0470621702 | 2012 | 400 pages | PDF | 14 MB