Bayesian Estimation

Description

This book has eight Chapters and an Appendix with eleven sections. Chapter 1 reviews elements Bayesian paradigm. Chapter 2 deals with Bayesian estimation of parameters of well-known distributions, viz., Normal and associated distributions, Multinomial, Binomial, Poisson, Exponential, Weibull and Rayleigh families. Chapter 3 considers predictive distributions and predictive intervals. Chapter 4 covers Bayesian interval estimation. Chapter 5 discusses Bayesian approximations of moments and their application to multiparameter distributions. Chapter 6 treats Bayesian regression analysis and covers linear regression, joint credible region for the regression parameters and bivariate normal distribution when all parameters are unknown. Chapter 7 considers the specialized topic of mixture distributions and Chapter 8 introduces Bayesian Break-Even Analysis. It is assumed that students have calculus background and have completed a course in mathematical statistics including standard distribution theory and introduction to the general theory of estimation.

First Sentence

Probability plays an important role in almost every field - industry, commerce, physical and biological sciences, statistics and of course, in our daily life.

Description

This work, Bayesian Estimation, by Professor Sinha, seeks to inform us why this should be so and is a valuable contribution to Statistics, bringing together both the interpretation and application of the Bayesian approach. Detailed mathematical proofs which students at this level like to see in an introductory text such as this, are included. This work is very well received by students, researchers and practicing statisticians.

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