Viewing: ST 440 / ST 540 : Applied Bayesian Analysis

Last approved: Wed, 10 Oct 2018 08:01:55 GMT

Last edit: Tue, 09 Oct 2018 13:01:51 GMT

Changes proposed by: boos
Catalog Pages referencing this course
Change Type
Minor
ST (Statistics)
440
032432
Dual-Level Course
Yes
540
Cross-listed Course
No
Applied Bayesian Analysis
Applied Bayesian Analysis
College of Sciences
Statistics (17ST)
27.0501
Statistics, General.
Term Offering
Spring Only
Offered Every Year
Previously taught as Special Topics?
Yes
2
 
Course Prefix/NumberSemester/Term OfferedEnrollment
ST 495/ST 590Spring 201517/42
ST 495/ST 590Spring 20166/30
Course Delivery
Face-to-Face (On Campus)
Distance Education (DELTA)
Online (Internet)

Grading Method
Graded with S/U option
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3.0
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Brian Reich
Associate Professor
full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture4040NoThis 40 is the combination of 440 and 540. The undergraduate 440 will have less than 10 in most semesters.
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Delivery FormatPer SemesterPer SectionMultiple Sections?Comments
LEC1515Noonline format is for graduate ST 540
Prerequisite: ST 422 and ST 430
Prerequisite: ST 512 or ST 514 or ST 515 or ST 516 or ST 517
Is the course required or an elective for a Curriculum?
No
Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

Addition of ST 516 to the list of viable prerequisites for the graduate version.


No

Is this a GEP Course?
No
GEP Categories

Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

Dr. Brian Reich will teach ST 440/540 and the PhD level course ST 740 as part of his standard teaching obligation; thus no new resources are required.

The goal of this course is to introduce Bayesian data analysis methods to students who do not have a theoretical background in statistics.

 


Student Learning Outcomes

Compute posteriors distributions for conjugate priors


Utilize various approaches for selecting prior distributions


Implement Markov chain Monte Carlo algorithms


Interpret output from software such as OpenBUGS and JAGS


Select appropriate statistical models and conduct goodness-of-fit diagnostics


Compare and contrast Bayesian versus frequentist methods


ST 540 Only: Demonstrate the ability to work with more theoretical aspects of selected topics via derivations, proofs, or other more advanced statistical techniques


Evaluation MethodWeighting/Points for EachDetails
Homework10%ST 540 will have additional questions that address the additional learning outcomes for graduate students.
Midterm30%Closed book, closed notes. ST 540 students will have additional Exam questions that address the additional learning outcomes for graduate students.
Midterm30%Closed book, closed notes. ST 540 students will have additional Exam questions that address the additional learning outcomes for graduate students.
Project30%A detailed analysis of a data set.
TopicTime Devoted to Each TopicActivity
Introduction to Bayesian statistics3 weeksreview of conditional probability and Bayes' rule; priors and posteriors; Bayesian learning; summarizing the posterior distribution

Methods for selecting prior distributions3 weeks basic conjugacy relationships; uninformative priors; reference priors; eliciting expert opinion
Bayesian computing3 weeksintroduction to R; Gibbs sampling; Metropolis sampling; introduction to OpenBUGS and PROC MCMC; convergence diagnostics
Bayesian linear and hierarchical models4 weeksZellner's prior; connections with classical least squares; random effects
Model selection and adequacy concepts1 weekBayes factors; DIC; residual analysis; sensitivity analysis; Bayesian p-values
Case studies1 weekComplete analysis of several complex datasets
Final exam1 weekfinal exam
mlnosbis 9/10/2018: This is a minor action updating the prerequistes. I removed the comments from previous action.
Key: 9461
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