ST 495B:
Practicum in Bayesian Inference
Fall Session, 2005


ST 495B Practicum in Bayesian Inference


M from 4:15 to 5:15 p.m.


203A Patterson Hall




Sujit Ghosh





203A Patterson Hall

Office hours:

Wednesday, 2:00 - 3:00 p.m. or 4:00 - 5:00 p.m. or by appointment

. .


Eric Kalendra & Norbert Kadima

Email: and

Class links: Lectures & Assignments| Ask a question

Course prequisite: ST 421 and ST 422

Required text: Bolstad, W. M. (2004). Introduction to Bayesian Inference. Wiley Interscience. (ISBN: 0471270202)

Statistical Resources: Bayesian Java Applications

Homework: Homework will normally be assigned weekly (as indicated on the homework page). Homework solution will normally be discussed on following week. Unexcused late homework will not be accepted. The final homework average will be computed after dropping the two lowest grades.

Project: Project will be assigned after the fall break (as indicated on the project page). Students will be required to work independently or as a group. As part of the project students will be required to obtain a set of real data, formulate suitable models and fit models using techniques learned in this course (e.g., the use of softwares like R or WinBUGS is highly encouraged). Students are required to present the project in a mini seminar style within a time slot of no more than 30 minutes. Each member of a group will receive equal credit.

Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course please log onto, follow the links to "ST" and "ST495" and click on "Message Board". Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message". You will receive a response from me or another student. Everyone in the class will be able see your question and the response.

Anonymous mail: If you wish to send me an anonymous suggestion or reminder, send email to The system will remove mail headers, but you must remember to removes your signature and other identifying information.

Grading System: Credit only (S or U). However an S or U will be determined based on a final score determined as follows:

Final Semester Score = (HW + P)/2

where HW is the homework average (out of 100) after dropping the two lowest scores and P is the scores (out of 100) on the project. A final score of 75 or better is required for an S.

Auditing:Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average. A homework score of 75 or better is required for an AU.

Policy on Academic Integrity: The University policy on academic integrity is spelled out in Appendix L of the NCSU Code of Student Conduct. For a more though elaboration see the NCSU Office of Student Conduct website. For this course group work on homework is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own.

Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students (DSS), 1900 Student Health Center, CB# 7509, 515-7653.

Reference material (Have requested these be on reserve at DH Hill Library):

DeGroot, Morris, H. and Schervish, Mark, J. (2001). Probability and Statistics, 3rd Edition. Addison Wesley.

Evans, Michael, J. and Rosenthal, Jeffrey, S. (2004). Probability and Statistics: The science of uncertainty. W. H. Freeman & Company.

Ross, Sheldon (2006). A First Course in Probability. Prentice Hall.

Course objectives:

A prime objective of the ST495B course is to present techniques and basic results of theory and application of Bayesian inference at an applied calculus level. In ST495B we develop the probabilistic language and computational tools of Bayesian statistics. The course describes probabilistic models for specifying prior distributions, summarizing posterior information, evaluating predictive distributions, formulating hierachical models and their application to solve real data problems. Students taking the course will have completed three semesters of calculus and had some exposure to basic probability and statistics.

Syllabus: In ST495B we shall cover most, but not all of the material in chapters 1 and 5 through 13 of Bolstad.
  1. Introduction Statistical science: process of learning, role of statistics in science
  2. Bayesian inference for single population: discrete random variables, continuous random variables
  3. Bayesian inferece for two populations: comparing proportions, comparing means, extensions to linear regression
  4. Comparing frequentist and Bayesian inferece: point estimates, interval estimates and test of hypothesis for proportion and means
  5. Bayesian linear regression: least square regression, predictive distributions, extensions to nonlinear regression

Last updated on: September 12, 2005