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

Bayesian analyses go beyond frequentist techniques of p-values and null hypothesis tests, providing a modern understanding of data analysis.

Todd E. Hudson (Author)

9781108812900, Cambridge University Press

Paperback / softback, published 24 June 2021

612 pages
25.2 x 20.2 x 3 cm, 1.44 kg

'This accessible, comprehensive textbook is a self-contained introduction to data analysis in the behavioral, neural, and biomedical sciences. Starting from logical first principles and requiring only minimal mathematical background, Hudson builds and explains the formal edifice of modern probability theory and data analysis. It is an impressive work.' Joachim Vandekerckhove, Associate Professor of Cognitive Sciences, University of California, Irvine, USA

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.

Subject Areas: Probability & statistics [PBT], Social research & statistics [JHBC], Research methods: general [GPS], Data analysis: general [GPH]