Summary

Bayesian Parametric Inference provides a systematic exposition and discusses in detail the conjugate and non-informative prior distributions, predictive distributions and their applications to problems of inventory control, finite populations, structural change in the model and control problems. Bansal consults information theoretic approach to construct maximal data information prior and maximum entropy priors in this book, alongside Bayesian decision theoretic approach, which 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, allowing the book to guide its reader to a comprehensive understanding of the topic.