Date Submitted: Mon, 12 Mar 2018 20:47:12 GMT

Viewing: ST 440 / ST 540 : Applied Bayesian Analysis

Last approved: Wed, 07 Mar 2018 16:08:33 GMT

Last edit: Wed, 07 Mar 2018 16:08:28 GMT

Changes proposed by: boos
Catalog Pages referencing this course
Change Type
Major
ST (Statistics)
440
032432
Dual-Level Course
Yes
540
Cross-listed Course
No
Applied Bayesian Analysis
Applied Bayesian Analysis
College of Sciences
Statistics (17ST)
Term Offering
Spring Only
Offered Every Year
Spring 2017
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
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
P: ST 422 and ST 430
P: ST 512 or ST 514 or ST 515 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.

The Bayesian approach to statistical analysis is becoming increasingly common across a wide variety of fields, and a new course is required to provide our students the background required to apply these methods in practice.    The course is designed for: (1) master's students in statistics to prepare them to apply Bayesian methods in their future careers; (2) graduate students in departments other than statistics to provide them with the analytic tools needed to carry out their thesis work; and (3) advanced undergraduate statistics majors to prepare them for graduate coursework in statistics.  Current Bayesian offerings (for example, ST 740) are designed primarily for Statistics PhD students, and this new class will shift the focus to benefit the three groups mentioned above.  In particular, it will deemphasize theoretical issues in favor of practical aspects of Bayesian data analysis such as computing using R and hierarchical modeling. 


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
ST 440 and 540 will differ in the following way. 540 will have an additional learning outcome: Demonstrate the ability to work with more theoretical aspects of selected topics via derivations, proofs, or other more advanced statistical techniques. 540 students will have additional HW and Exam questions to evaluate this learning outcome. The projects for 540 students will be expected to be on more advanced topics and have more depth than those for 440.

Comment on anticipated impact of establishment of ST 440/540 on ST 740 (PhD-level course Bayesian Inference): ST 540 may take a few students from ST 740, but we hope that ST 540 will attract students who might not have taken ST 740.

mlnosbis 1/10/2017: Previous enrollment shows demand for the course. Though similar to ST 740, this course is available to non-ST and non-PhD students.

pjharrie 1/19/2017: there is a disconnect between the grading scheme - which states that grad students will have different tests and the statement in the additional comments which appears to be repeated from the other course proposals. So which is it?

ABGS Reviewer Comments:
-Do we want additional detail about the additional homework and additional exam questions for the 500-level? It seems that in the past we have asked for clearer articulation.

pjharrie 1/31/2017 I think the more clearly the differences between 400/500 levels are articulated, the better. So, I feel that the ABGS Reviewer comments should be addressed.
allloyd (Mon, 07 Nov 2016 00:51:52 GMT): Passed college grad committee 11/4/2016
Key: 9461