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Welcome to Bayesian Statistical Methods by Reich and Ghosh, Second Edition!

This book is intended as a reference for practioners and as the course textbook for advanced undergraduate and graduate students. It assumes the basic calculus background needed to convey ideas precisely, but no prior experience with advanced statistical methods. The chapters progress from the foundational concepts that define Bayesian statistics to methods that cover the spectrum from simple linear regression through deep learning, with a consistent focus on practical aspects including selecting an appropriate method, computation and summarizing the results.

BSM2 front cover

New to the second edition:

  • A new chapter on Machine learning including Bayesian Additive Regression Trees and deep learning with R code and examples
  • Expanded worked examples including causal inference and equation learning
  • Advanced computational methods including variational and approximate Bayes computing
  • Many new homework problems with partial solutions
  • Latex slides for all chapters
  • View the book for free!