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Probability Matching Priors: Higher Order Asymptotics

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Release : 2012-12-06
Genre : Mathematics
Kind : eBook
Book Rating : 36X/5 ( reviews)

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Book Synopsis Probability Matching Priors: Higher Order Asymptotics by : Gauri Sankar Datta

Download or read book Probability Matching Priors: Higher Order Asymptotics written by Gauri Sankar Datta. This book was released on 2012-12-06. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.

Probability Matching Priors

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Author :
Release : 2004-01-01
Genre :
Kind : eBook
Book Rating : 374/5 ( reviews)

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Book Synopsis Probability Matching Priors by : Gauri Sankar Datta

Download or read book Probability Matching Priors written by Gauri Sankar Datta. This book was released on 2004-01-01. Available in PDF, EPUB and Kindle. Book excerpt:

Higher Order Asymptotics

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Author :
Release : 1994
Genre : Mathematics
Kind : eBook
Book Rating : 317/5 ( reviews)

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Book Synopsis Higher Order Asymptotics by : J. K. Ghosh

Download or read book Higher Order Asymptotics written by J. K. Ghosh. This book was released on 1994. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Thinking, Modeling and Computation

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Release : 2005-11-29
Genre : Mathematics
Kind : eBook
Book Rating : 174/5 ( reviews)

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Book Synopsis Bayesian Thinking, Modeling and Computation by :

Download or read book Bayesian Thinking, Modeling and Computation written by . This book was released on 2005-11-29. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics

Objective Bayesian Inference

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Release : 2024-03-06
Genre : Mathematics
Kind : eBook
Book Rating : 92X/5 ( reviews)

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Book Synopsis Objective Bayesian Inference by : James O Berger

Download or read book Objective Bayesian Inference written by James O Berger. This book was released on 2024-03-06. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications.

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