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Spring 2021 Statistics GR6103 section 001

Call Number 13277
Day & Time
M 3:10pm-6:00pm
Points 4
Grading Mode Standard
Approvals Required Instructor
Instructor Samory Kpotufe
Method of Instruction On-Line Only
Course Description Prerequisites: STAT GR6102 Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss the relevant theory and get to open questions in model building, computing, evaluation, and expansion. The course is intended for students who want to do applied statistics and also those who are interested in working on statistics research problems.
Web Site Vergil
Department Statistics
Enrollment 12 students (25 max) as of 9:06PM Friday, November 26, 2021
Subject Statistics
Number GR6103
Section 001
Division Graduate School of Arts and Sciences
Open To GSAS
Campus Morningside
Note PhD students in Statistics only
Section key 20211STAT6103G001

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SIS update 11/26/21 21:06    web update 11/26/21 21:23