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Bayesian Data Analysis for the Behavioral and Neural Sciences: 
Non-Calculus Fundamentals

Chapter 1: Logic and Data Analysis
Chapter 2: Mechanics of Probability Calculations
Chapter 3: Probability and Information: from Prior to Posterior Probabilities
Chapter 4: Prediction and Decision
Chapter 5: Models and Measurements
Chapter 6: Model Selection

Appendix A: Programming Basics
Appendix B: Exponents and Logarithms
Appendix C: The Bayesian Toolbox:
       Marginalization and Coordinate Transformation

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond ‘frequentist’ concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, and demonstrates analysis techniques using over 100 worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called ‘hypothesis testing’) problems most frequently encountered in real-world applications.

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