Bayesian Data Analysis for the Behavioral Sciences

ABOUT THE COURSE: This introductory course is designed to equip students with a conceptual understanding of Bayesian inference and its applications in behavioral data analysis and modeling. By the end of this course, students will be able to: (i) analyze data in the Bayesian framework, (ii) develop and implement Bayesian models, and (iii) evaluate computational (cognitive) models given the data.INTENDED AUDIENCE: Data science students, Cognitive Science students, Behavioral analytics studentsPREREQUISITES: The only prerequisite is R programming. Mathematics background up to class 12 is desired.To prepare for this course, participants can do courses like: ‘Foundations of R Software’ available on NPTEL taught by Prof. Shalabh, IIT KanpurINDUSTRY SUPPORT: Data Science, Behavioral Analytics, Statistical Analytics Consultancy, Pharmaceutical

Syllabus

Week 1: Probability and Random Variables I
Week 2:Probability and Random Variables II
Week 3:Bayes’ theorem, likelihood function, prior distribution, and posterior distribution
Week 4:Parameter estimation I: Analytical methods and grid approximation
Week 5:Parameter estimation II: Markov Chain Monte Carlo
Week 6:Parameter estimation III: Hamiltonian Monte Carlo and the brms package
Week 7:Bayesian regression modeling
Week 8:Model comparison I: Cross validation
Week 9:Model comparison II: Bayes factors
Week 10:Bayesian hierarchical modeling
Week 11:Bayesian modeling with Stan
Week 12:Mixture models and multinomial processing trees