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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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