Bayesian Parametric Inference provides a systematic exposition and discusses in detail the conjugate and noninformative prior distributions, predictive distributions and their applications to problems of inventory control, finite populations, structural change in the model and control problems. Information theoretic approach to construct maximal data information prior and maximum entropy priors is also discussed. Bayesian decision theoretic approach is followed to obtain Bayes estimates under various loss functions. The concept of Bayes Factor for comparing hypotheses is explained with the help of some simple but illustrative examples.

• · More than 300 Solved Examples · 250 Unsolved Exercises · 350 Remarks · Glossary of Bayesian Terms · Exhaustive List of References

Foreword / Preface / Probability, Random Variables and their Probability Distributions / Some Special Distributions / Bayes Theorem / Conjugate Prior Distribution / Non-Informative Priors / Bayes Estimation / Hypothesis Testing / Predictive Inference / Bayesian Inference for the Linear Model / Large Sample Approximations / Other Topics / Question Bank / Glossary / Tables / Bibliography / Index.

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