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Lee, Peter M. - Bayesian Statistics: An Introduction, ebook

Bayesian Statistics: An Introduction

Lee, Peter M.


Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based

Box, George E. P. - Bayesian Inference in Statistical Analysis, ebook

Bayesian Inference in Statistical Analysis

Box, George E. P.


Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential

Congdon, Peter - Bayesian Statistical Modelling, ebook

Bayesian Statistical Modelling

Congdon, Peter


Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods

Bernardo, José M. - Bayesian Theory, ebook

Bayesian Theory

Bernardo, José M.


This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development

Congdon, Peter - Bayesian Models for Categorical Data, ebook

Bayesian Models for Categorical Data

Congdon, Peter


The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian

Haug, Anton J. - Bayesian Estimation and Tracking: A Practical Guide, ebook

Bayesian Estimation and Tracking: A Practical Guide

Haug, Anton J.


Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices.
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