中文参考译名:贝叶斯分析，贝叶斯计量经济学（研究鲁棒性）
Author: Joseph B. Kadane
Publisher: Elsevier Science Ltd
Keywords: bayesian, econometrics, studies, analyses, robustness
Number of Pages: 326
Published: 1984-05-01
List price: unknow
ISBN-10: 0444862099
ISBN-13: 9780444862099

Author: James O. Berger et al.
Publisher: Institute of Mathematical Statistics
Keywords: bayesian, robustness, essays, studies, century, amp, italy, proceedings, workshop, rimini, sixteenth
Number of Pages: 353
Published: 1996-01
List price: unknow
ISBN-10: 0940600412
ISBN-13: 9780940600416

Authors:Jean-Michel Marin, Christian Robert,
Publisher: Springer
Keywords: statistics, bayesian, texts, springer, approach, core, practical, computational
Number of Pages: 258
Published: 2007-02-02
List price: $89.95
ISBN-10: 0387389792
ISBN-13: 9780387389790

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to l

中文参考译名:贝叶斯计算与R（用R）的
Author: Jim Albert
Publisher: Springer
Keywords: computation, bayesian
Number of Pages: 300
Published: 2009-05-15
List price: $59.95
ISBN-10: 0387922970
ISBN-13: 9780387922973

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R’s open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. Th

中文参考译名:贝叶斯计算与R（使用注册商标）
Author: Jim Albert
Publisher: Springer
Keywords: computation, bayesian
Number of Pages: 270
Published: 2008-06-11
List price: $54.95
ISBN-10: 0387713840
ISBN-13: 9780387713847

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R’s open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. T

中文参考译名:贝叶斯计量经济学
Author: Professor Gary Koo
Publisher: Wiley-Interscience
Keywords: econometrics, bayesian
Number of Pages: 374
Published: 2003-07-16
List price: unknow
ISBN-10: 0470845678
ISBN-13: 9780470845677

Bayesian Econometrics introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. It includes numerous numerical examples and topics covered in the book include: the regression model (and variants applicable for use with panel data time series models models for qualitative or censored data nonpa

Authors:J.K. Ghosh, R.V. Ramamoorthi,
Publisher: Springer
Keywords: nonparametrics, bayesian
Number of Pages: 304
Published: 2003-04-08
List price: $104.00
ISBN-10: 0387955372
ISBN-13: 9780387955377

Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonpar