Indiana University Bloomington

2015

Psychological and Brain Science P534
Introduction To Bayesian Data Analysis 2

Contact: John Kruschke (kruschke@indiana.edu)
Capacity: 40
Sequence: This is the second of a two-course sequence. The first is P533.
Pre-Requisites: P533.
Algebra Required: No matrix algebra used.
Calculus Required: Calculus is not needed for assignments; is occasionally used for concepts and explanations.
Contact Person for Authorization: None.
Instructor: John Kruschke
Days Per Week Offered: 8-week format, with two 75 minute meetings per week.
Website: http://www.indiana.edu/~jkkteach/P533/
Recommended follow-up classes: any
Syllabus: No Syllabus Avaliable
Keywords: Bayesian, proportions, means, analysis of variance, regression, logistic, ordinal, probit, categorical
Description: P533/P534 is a tutorial introduction to doing Bayesian statistics for data analysis. The course is intended to make advanced Bayesian methods genuinely accessible to real graduate students, and even unreal undergraduates (see pre-req's below). Many complete computer programs are provided for you do adapt to your own research. In P533, we start from the basics of probabilities and Bayes' theorem, and gradually work our way through contemporary Monte Carlo methods in the context of simple analyses, building up to simple linear regression and Bayesian versions of single-factor analysis of variance (ANOVA). In P534, we contrast null hypothesis significance testing with Bayesian approaches to null value assessment, as well as Bayesian approaches to power. Then we do a variety of more complicated realistic applications, covering the Bayesian versions of multiple linear regression, logistic regression, analysis of variance, etc., including a look at repeated measures designs.
Books: Kruschke, J. K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Academic Press.
Substantive Orientation: Any.
Statistical Orientation: Bayesian, all models.
Applied/Theoretical: Applied with thorough explanations.
Software: R
How Software is Used: For data analysis. Students also modify programs to adapt to different applications.
Problem Sets: Weekly.
Data Analysis: As part of assignments.
Presentation: None.
Exams: None.
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