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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.

Fall 2022 Statistics GR6103 section 001

Call Number 13863
Day & Time
W 1:30pm-3:30pm
303 Hamilton Hall
Points 4
Grading Mode Standard
Approvals Required None
Instructor Liam M Paninski
Method of Instruction In-Person
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 2 students (25 max) as of 5:33PM Monday, September 26, 2022
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 20223STAT6103G001

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SIS update 09/26/22 17:33    web update 09/26/22 21:16