Eye-catching Statistical Rethinking : A Bayesian Course With Examples In R And Stan Instant 55% Discount [hf5KmVar]

Statistical Rethinking: A Bayesian Approach

Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a comprehensive guide designed to enhance readers' understanding and confidence in statistical modeling. This book stands out by emphasizing the computational aspect of statistics, encouraging readers to engage in hands-on calculations rather than relying solely on automated tools. By doing so, it fosters a deeper understanding of the intricacies of statistical modeling, allowing for more informed choices and interpretations in one's own work.

Key Features and Specifications

Written by a renowned expert in the field, this hardback book offers 487 pages of in-depth knowledge. It covers a broad range of topics, from the basics of regression to advanced multilevel models, all from a Bayesian perspective. The text also delves into critical issues such as measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. Key features include:

• Bayesian Probability and Maximum Entropy: A simple logical interpretation of Bayesian probability and maximum entropy provides a strong foundation for understanding complex models.
• Practical Examples: Complete R code examples throughout the book allow readers to apply the concepts to real-world data, ensuring practical skills development.
• Web Resource: Accompanied by an R package (rethinking) with core functions for constructing various statistical models.

Practical Applications and Benefits

Statistical Rethinking is an invaluable resource for PhD students and seasoned professionals in the natural and social sciences. It prepares readers for more advanced statistical modeling by providing a solid foundation in Bayesian techniques. The book's unique approach to statistical rethinking ensures that readers not only learn the concepts but also gain the confidence to apply them in their own research. By focusing on the computational side of statistics, this book equips readers with the skills needed to make informed decisions and interpretations in their statistical work. With its comprehensive coverage, practical examples, and accompanying resources, Statistical Rethinking is an essential tool for anyone seeking to master Bayesian statistical modeling.