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Bayesian Nonparametrics (Springer Series in Statistics) by R.V. Ramamoorthi [Repost]
Author: BUGSY

Bayesian Nonparametrics (Springer Series in Statistics) by R.V. Ramamoorthi
English | Apr 8, 2003 | ISBN: 0387955372 | 319 Pages | PDF | 2 MB

This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
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Bayesian Analysis with Python
Author: AlenMiler

Bayesian Analysis with Python by Osvaldo Martin
English | 25 Nov. 2016 | ISBN: 1785883801 | 282 Pages | AZW3/MOBI/EPUB/PDF (conv) | 36.2 MB
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Bayesian Analysis with Python
Author: readerXXI

Bayesian Analysis with Python
by Osvaldo Martin
English | 2016 | ISBN: 1785883801 | 282 Pages | True PDF | 4.5 MB

The purpose of this book is to teach the main concepts of Bayesian data analysis. Details
Reliability and Risk: A Bayesian Perspective (Repost)
Author: elodar

Nozer D. Singpurwalla, "Reliability and Risk: A Bayesian Perspective"
English | 2006-09-11 | ISBN: 0470855029 | 381 pages | PDF | 3.2 mb
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Bayesian Time Series Models (repost)
Author: roxul

David Barber, A. Taylan Cemgil, "Bayesian Time Series Models"
English | 2011 | ISBN: 0521196760 | 432 pages | PDF | 10,5 MB
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Bayesian Nonparametrics (Repost)
Author: step778

J.K. Ghosh, R.V. Ramamoorthi, "Bayesian Nonparametrics"
2003 | pages: 318 | ISBN: 0387955372 | PDF | 1,4 mb
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Bayesian Time Series Models
Author: roxul

David Barber, A. Taylan Cemgil, "Bayesian Time Series Models"
English | 2011 | ISBN: 0521196760 | 432 pages | PDF | 10,5 MB
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Bayesian Nonparametrics
Author: Specialselection

J.K. Ghosh, R.V. Ramamoorthi, "Bayesian Nonparametrics"
Springer; 1 edition | English | 2003-04-08 | ISBN: 0387955372 | 318 pages | PDF | 1.4 mb
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Risk and Asset Allocation
Author: Mazepa777

Instant Info Riches
Publisher Springer-Verlag Berlin Heidelberg New York | ISBN-10: 3540222138 | edition 2005 | PDF | 547 pages | 11.76 mb

This encyclopedic, detailed exposition spans all the steps of one-period allocation from the foundations to the most advanced developments.
Multivariate estimation methods are analyzed in depth, including non-parametric, maximum-likelihood under non-normal hypotheses, shrinkage, robust, and very general Bayesian techniques. Evaluation methods such as stochastic dominance, expected utility, value at risk and coherent measures are thoroughly discussed in a unified setting and applied in a variety of contexts, including prospect theory, total return and benchmark allocation.
Portfolio optimization is presented with emphasis on estimation risk, which is tackled by means of Bayesian, resampling and robust optimization techniques.
All the statistical and mathematical tools, such as copulas, location-dispersion ellipsoids, matrix-variate distributions, cone programming, are introduced from the basics. Comprehension is supported by a large number of figures and examples, as well as real trading and asset management case studies.
At symmys.com the reader will find freely downloadable complementary materials: the Exercise Book; a set of thoroughly documented MATLAB® applications; and the Technical Appendices with all the proofs. More materials and complete reviews can also be found at symmys.com. Details
Attilio Meucci, "Risk and Asset Allocation (Springer Finance)" (Repost)
Author: s13tas

Attilio Meucci, "Risk and Asset Allocation (Springer Finance)" (Repost)
Publisher: Springer | ISBN: 3540222138 | edition 2005 | PDF | 547 pages | 11,76 mb

This encyclopedic, detailed exposition spans all the steps of one-period allocation from the foundations to the most advanced developments. Multivariate estimation methods are analyzed in depth, including non-parametric, maximum-likelihood under non-normal hypotheses, shrinkage, robust, and very general Bayesian techniques. Evaluation methods such as stochastic dominance, expected utility, value at risk and coherent measures are thoroughly discussed in a unified setting and applied in a variety of contexts, including prospect theory, total return and benchmark allocation. Details
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