A First Course In Bayesian Statistical Methods

Author: Peter D. Hoff
Publisher: Springer Science & Business Media
ISBN: 9780387924076
Size: 67.46 MB
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

A First Course In Bayesian Statistical Methods

Author: Peter D. Hoff
Publisher: Springer
ISBN: 9781441928283
Size: 21.40 MB
Format: PDF, ePub
View: 1646
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

A First Course In Bayesian Statistical Methods

Author: Peter D. Hoff
Publisher: Springer
ISBN: 9780387922997
Size: 34.91 MB
Format: PDF, Kindle
View: 898
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Monte Carlo Statistical Methods

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 1475730713
Size: 79.33 MB
Format: PDF, Kindle
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We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

The Bayesian Choice

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 0387715991
Size: 27.51 MB
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This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.

Bayesian Essentials With R

Author: Jean-Michel Marin
Publisher: Springer Science & Business Media
ISBN: 1461486874
Size: 10.97 MB
Format: PDF, Kindle
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This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. 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 lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.

An Introduction To Bayesian Analysis

Author: Jayanta K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387354336
Size: 11.20 MB
Format: PDF, ePub, Docs
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This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.

Bayesian Core A Practical Approach To Computational Bayesian Statistics

Author: Jean-Michel Marin
Publisher: Springer Science & Business Media
ISBN: 0387389792
Size: 39.83 MB
Format: PDF, Mobi
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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, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications.

Statistical Models And Methods For Financial Markets

Author: Tze Leung Lai
Publisher: Springer Science & Business Media
ISBN: 0387778276
Size: 14.54 MB
Format: PDF, Kindle
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The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.

Introduction To Applied Bayesian Statistics And Estimation For Social Scientists

Author: Scott M. Lynch
Publisher: Springer Science & Business Media
ISBN: 0387712658
Size: 66.20 MB
Format: PDF, ePub, Mobi
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This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.