Directory of Classes

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 GR5224 section 001

Call Number 13838
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/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.
Web Site Vergil
Department Statistics
Enrollment 34 students (86 max) as of 5:33PM Monday, September 26, 2022
Subject Statistics
Number GR5224
Section 001
Division Interfaculty
Open To GSAS
Campus Morningside
Note STAT MA students only
Section key 20223STAT5224W001

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