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

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, which demonstrates analysis techniques throughout with 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.

1. Logic and data analysis; 2. Mechanics of probability calculations; 3. Probability and information: from priors to posteriors; 4. Prediction and decision; 5. Models and measurements; 6. Model selection: Appendix A. Coding basics; Appendix B. Mathematics review: logarithmic and exponential function; Appendix C. The Bayesian toolbox: marginalization and coordinate transformations.

Todd E. Hudson is a professor of rehabilitation medicine at New York University's Grossman School of Medicine, holding cross-appointments in neurology, and also in the Department of Biomedical Engineering at the New York University Tandon School of Engineering. Dr Hudson has taught statistics, perception and sensory processes, experimental design, and/or advanced topics in neurobiology and behavior at several major universities, including Brandeis University and Columbia University. He co-founded, and serves as Chief Scientific Advisor to, Tactile Navigation Tools, LLC, which develops navigation aids for the visually impaired.