Preface

This book provides a short, accessible introduction to applied probabilistic modeling and statistical inference.1 Application areas include the physical sciences, biological sciences, social sciences, engineering, health and medicine, business and finance, education, government, and sport and leisure—anywhere there is data from which we want to draw conclusions or make decisions.

Goal

After reading this book, you should have a solid grasp of how to draw sound inferences from noisy measurements and how to make reasoned decisions in the face of uncertainty.

Approach

This book differs from other books covering similar material in that it

  • is short, top-down, and example-driven;
  • assumes only introductory-level mathematics and computation;2 Specifically, this book assumes some familiarity with algebra, integral calculus, and exposure to basic programming concepts.
  • develops probability theory and statistical inference through simulation;
  • takes an unapologetically Bayesian approach to modeling and inference;
  • is fully reproducible with open-source code, figures, and text.

Licensing

The text of this book is distributed under the CC BY-ND 4.0 license. The accompanying code is distributed under the BSD 3-clause license.

Acknowledgements

Thanks to Mitzi Morris for proofreading.