Bayesian Analysis of Structural Change in Linear Models

This book provides a comprehensive introduction to Bayesian analysis and its application to the detection of structural change in linear models. The author, S. Sundarabalan, uses a practical approach that combines theory and examples to help readers develop a strong understanding of the subject matter.

Through the use of Bayesian techniques, readers will learn how to model structural changes in time series and cross-sectional data. The book covers a range of important topics, including model selection, model comparison, and hypothesis testing. It also provides guidance on how to choose prior distributions and how to use Markov Chain Monte Carlo (MCMC) methods for posterior inference.

The book is ideal for students and researchers who have a basic understanding of linear models and want to learn more about Bayesian analysis. It is written in a clear and accessible style, with numerous examples and exercises that reinforce the material covered in each chapter.

With its focus on practical applications, this book is an essential resource for anyone interested in using Bayesian analysis to detect structural changes in linear models.