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Bayesian analyses go beyond frequentist techniques of p-values and null hypothesis tests, providing a modern understanding of data analysis.
List 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.
About the author
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.
Summary
This textbook teaches undergraduates in psychology, neuroscience, and medicine modern data analysis techniques. It uses non-calculus-based mathematics with examples specific to behavioral and neural sciences. Perfect for statistics courses, it shows students how to write code for their own data analyses, even those involving individual differences.
Additional text
'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
