Bayesian Methods

This insightful book, Bayesian Methods, explores the Bayesian approach to statistics with a practical focus, making it ideal for students and researchers alike.

The book Bayesian Methods offers a comprehensive exploration of the Bayesian approach to statistics, tailored specifically for final year undergraduate and Masters students, as well as statistical and interdisciplinary researchers. This text stands out by integrating Bayesian statistics with a practical emphasis on mainstream methodologies, demonstrating how to derive scientific, medical, and social insights from numerical data. The authors, drawing on extensive experience in both practical applications and research, provide a wealth of statistical techniques that are not readily available in other resources.

The initial chapter introduces Fisherian methods, setting a solid foundation for readers. The book maintains a strong focus on likelihood, making it relevant for mainstream statistics courses that may adopt mixed or comparative philosophies. Subsequent chapters delve into crucial topics such as linear models, categorical data analysis, time series forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. Each chapter is enriched with practical examples, worked solutions, and exercises to reinforce learning.

Bayesian Methods is an essential resource for statisticians, statistics students, and interdisciplinary researchers alike. By bridging the gap between Bayesian and traditional statistical approaches, it equips readers with the tools necessary to make informed conclusions from data across various fields, enhancing their understanding and application of statistical principles.