Bayesian Data Analysis for the Behavioral and Neural Sciences
Bayesian Data Analysis for the Behavioral and Neural Sciences
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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.
- Presents Bayesian statistics from a non-calculus perspective
- Provides in-text computer code to bypass the need for advanced mathematics and coaches students through writing basic computer code to implement their data analyses
- Shows students how to think through the steps necessary to tailor their analysis to any experimental scenario
- Focuses on worked examples from the behavioral and neural sciences to explain the theory
Reviews & endorsements
'Todd E. Hudson's book is very readable and nicely put together. It should be a useful addition to the growing Bayesian literature aimed at university students.' D. S. Sivia, College Lecturer, St Catherine's College, Oxford, UK
'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
Product details
June 2021Hardback
9781108835565
612 pages
259 × 207 × 32 mm
1.56kg
Available
Table of Contents
- 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.
