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Bayesian Data Analysis for the Behavioral and Neural Sciences


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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.

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

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.


'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

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