Bayes Rules!: An Introduction to Applied Bayesian Modeling

Bayes Rules! is a comprehensive introduction to Bayesian statistics that empowers readers to apply modern Bayesian thinking, modeling, and computing to a broad audience. It assumes familiarity with undergraduate-level statistics and probability and provides R code for implementation. The book emphasizes practical and generalizable model building, iterative evaluation, and Markov chain Monte Carlo simulation, with data-driven examples and exercises. It is ideal for advanced undergraduate statistics students and practitioners with comparable experience.

Format: Paperback / softback
Length: 521 pages
Publication date: 04 March 2022
Publisher: Taylor & Francis Ltd

Bayes Rules!: An Introduction to Applied Bayesian Modeling is a captivating and sophisticated guide to the realm of Bayesian statistics. Written with a broad audience in mind, it brings the power of modern Bayesian thinking, modeling, and computing to a wide range of readers. Specifically designed for advanced undergraduate statistics students and practitioners with comparable experience, the book assumes a foundational understanding of introductory statistics and probability, calculus, and the R statistical software.

Despite its technical nature, Bayes Rules! employs a user-friendly and engaging writing style, making it accessible to individuals with varying backgrounds. The book begins by introducing fundamental Bayesian concepts and principles, such as probability distributions, prior knowledge, and Bayes' theorem. It then delves into the practical application of Bayesian methods, covering topics such as multivariable regression, hierarchical modeling, and Markov chain Monte Carlo simulation.

Throughout the text, data-driven examples and exercises help reinforce the theoretical concepts and facilitate a deeper understanding of the subject matter. The iterative model building and evaluation process, emphasized throughout the book, enables readers to develop practical and generalizable models that can be applied to real-world data analysis.

One of the key strengths of Bayes Rules! is its comprehensive coverage of multivariable regression and classification models. The book surveys a wide range of these models, from simple linear regression to complex nonlinear models, and provides detailed explanations and examples to aid in their understanding and implementation. Fundamental Markov chain Monte Carlo simulation is also introduced, providing a powerful tool for Bayesian inference and uncertainty quantification.

To enhance the learning experience, the book integrates R code, including RStan modeling tools and the bayesrules package. This allows readers to apply the theoretical concepts directly to their data analysis and gain hands-on experience with Bayesian modeling techniques. The inclusion of R code also makes the book accessible to individuals who may not be familiar with programming languages.

In addition to its technical content, Bayes Rules! encourages readers to tap into their intuition and learn by doing. The book provides a friendly and inclusive introduction to technical Bayesian concepts, making it an ideal resource for both beginners and experienced practitioners seeking to expand their knowledge and skills in the field. Furthermore, the book supports Bayesian applications with foundational Bayesian theory, providing a solid foundation for further research and development in the area.

Overall, Bayes Rules!: An Introduction to Applied Bayesian Modeling is a highly recommended resource for advanced undergraduate statistics students, practitioners, and anyone interested in learning about Bayesian statistics and its practical applications. With its engaging writing style, comprehensive coverage, and practical examples, the book empowers readers to weave Bayesian approaches into their everyday practice and gain a deeper understanding of the field.

Weight: 1090g
Dimension: 225 x 285 x 31 (mm)
ISBN-13: 9780367255398