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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.


Summer 2021 Statistics GU4224 section 001
BAYESIAN STATISTICS

Call Number 11092
Day & Time
Location
MTWR 9:00am-10:35am
ONLINE ONLY
Points 3
Grading Mode Standard
Approvals Required None
Instructor Ronald Neath
Type LECTURE
Method of Instruction On-Line Only
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
Subterm 05/03-06/14 (A)
Department Summer Session (SUMM)
Enrollment 6 students (35 max) as of 9:12PM Monday, November 29, 2021
Subject Statistics
Number GU4224
Section 001
Division Summer Session
Open To Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies, Professional Studies, Summer Session
Campus Morningside
Section key 20212STAT4224W001

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SIS update 11/29/21 21:12    web update 11/29/21 21:38