Objective Bayesian Inference
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€167.40
A01=Dongchu Sun
A01=James O Berger
A01=Jose-miguel Bernardo
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Age Group_Uncategorized
Author_Dongchu Sun
Author_James O Berger
Author_Jose-miguel Bernardo
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Bivariate Normal Distribution
Category1=Non-Fiction
Category=PBTB
Coherence
Conditioning
Confidence Distributions
COP=Singapore
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eq_isMigrated=0
eq_isMigrated=2
eq_nobargain
Fiducial Inference
Frequentist Matching
Grouped Reference Priors
Hierarchical Modelling
Hierarchical Normal Models
Improper Priors
Invariance
Inverse Probability
Jeffreys Prior
Language_English
Markov Chain Monte Carlo
Matching Priors
Maximizing Missing Information
Multinomial Distribution
Non-Regular Models
Objective Bayes
Overall Objective Priors
PA=Available
Partial Information
Price_€100 and above
PS=Active
Quantity of Interest
Reference Priors
Reverse Reference Priors
Sequential
softlaunch
Star-shaped Models
Subjective Bayes
The Constant Prior
Vague Proper Priors
Product details
- ISBN 9789811284908
- Publication Date: 26 Mar 2024
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
- Language: English
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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.
