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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 GU4224 section 001

Call Number 13804
Day & Time
MW 6:10pm-7:25pm
501 Schermerhorn Hall [SCH]
Points 3
Grading Mode Standard
Approvals Required None
Instructor Ronald Neath
Method of Instruction In-Person
Course Description This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software. Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205.  
Web Site Vergil
Department Statistics
Enrollment 28 students (35 max) as of 5:33PM Monday, September 26, 2022
Subject Statistics
Number GU4224
Section 001
Division Interfaculty
Open To Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies, SIPA, Professional Studies
Campus Morningside
Section key 20223STAT4224W001

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