a pdf version of syllabus - NCSU Statistics

ST 790: Advanced Bayesian Inference http://www4.stat.ncsu.edu/~sghosh2/TEACHING/st790abi/
Course: ST790C: Advanced Bayesian Inference
Time: TH from 11:45AM to 1:00PM
Place: 1108 SAS Hall
ST790-001
Advanced Bayesian Inference
Spring Session, 2013
(a pdf version of syllabus)
Instructor: Sujit Ghosh
Email: sujit.ghosh@ncsu.edu
Telephone 515-1950
Office: 5112 SAS Hall
Office
hours:
Tue/Thu 3:00 - 5:00 p.m. or by appointment
Class links: Lectures Slides| Course resources| Ask a question (use Message board)
Course prerequisite: ST522 and corequisite: ST740
Suggested Texts:
Robert, C. and Casella, G. (2004). Monte Carlo Statistical Methods. Springer, New
York (ISBN 978-0-387-21239-5)
Ghosh, J.K. and Ramamoorthi, R.V. (2003). Bayesian Nonparametrics. Springer,
New York (ISBN 978-0-387-95537-7)
Homework: Homework will normally be assigned at the end of class on alternate
Tuesdays. Homeworks will not be graded.
Examinations: There will be no in-class examinations. Students are however
required to submit two project works.
Project schedule:
Midterm
project
Final
project
Friday, March 01
(Abstract due
March 22) Friday,
May 03
by
5:00p.m.
by
5:00p.m.
Electornic
submission
Electronic
Submission +
In-class
presentations
Project
description
Project
description
Asking questions: If you have questions about lectures, assignments, exams,
procedures or any other aspect of the course please log onto
http://courses.ncsu.edu/st790/, 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 ST790-001@wolfware.ncsu.edu. The system will remove
mail headers, but you must remember to remove your signature and other
identifying information.
Grading System: Final grade will be based on:
Course objectives:
This course is an experimental
offering focused on the Bayesian
inferential methods with
emphasis on theory and
applications. The essence of
Bayesian methods is based on
the concept of updating evidence
using formal probabilistic rules.
This course will begin with a
discussion of foundational
issues, and then progress to its
use as a general paradigm for
statistical methodology. Recent
developments of computational
tools have brought Bayesian
treatment of realistic, complex
problems within the reach of
practicing statisticians. Some of
the more specialized
computational techniques will be
discussed as well as their
implementation for important
applications. In recent years
substantial progress has been
made in developing a theory for
infinite dimensional models and
toward the implementation of
the Bayesian computational
methods for variable
dimensional models. This course
will illustrate a variety of
theoretical and computational
methods, simulation techniques,
and hierarchical models suitable
for analyzing independent and
correlated data. The three broad
topics on the course include (i)
Monte Carlo methods based on
Markov Chain theory, (ii)
Bayesian large sample methods
and (iii) Bayesian nonparametric
models.
Students taking the course will
have completed both ST522
and/or ST740.
Syllabus: In ST790-ABI we
shall complete the following
concepts.
Basics Bayesian Inference
(about 5 lectures): See
lecture slides
1 of 2 Printed by Sujit Ghosh 12/17/2012 3:43 PM

ST 790: Advanced Bayesian Inference http://www4.stat.ncsu.edu/~sghosh2/TEACHING/st790abi/
Final Semester Score = (CP + 4xMP + 5xFP)/10
where CP is based on class participation and MP and FP are the scores (out of
100) on the midterm and the final projects, respectively. Grades will be assigned
on the +/- scale.
Auditing: Auditors are expected to attend class regularly and submit midterm on
the same schedule as the other students. The final grade for auditors (AU or NR)
will be based on their midterm scores. A midterm score of 75 or better is required
for an AU.
Policy on Academic Integrity: The University policy on academic integrity is
spelled out in 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):
Carlin, B. P. and Louis, T. A. (2008). Bayesian Methods for Data Analysis (Third
Edition), CRC Press, New York.
Marin, J. M. and Robert, C. P. (2007). Bayesian Core: A practical Approach to
Computational Bayesian Statistics, Springer, New York.
Monte Carlo methods for
Bayesian computation
(about 10 lectures):
Materials are primarily
based on the following
sections of the Robert &
Casella's book titled
"Monte Carlo Statistical
Methods:"
Chap.2: 2.1.2, 2.3,
2.4
Chap.3: 3.2, 3.3.1,
3.3.2
Chap.4: 4.1, 4.2, 4.3
Chap.6: 6.2, 6.3.1,
6.4.3, 6.5, 6.6, 6.7,
6.9.3, 6.9.4
Chap.7: 7.2, 7.3,
7.4, 7.5
Chap.8: 8.1, 8.2, 8.3
Chap.9: 9.1.2, 9.2.2,
9.2.3
Chap.10: 10.1.1,
10.1.2, 10.2.1,
10.2.2
Bayesian Large Sample
Methods (about 3
lectures): Materials are
primarily based on the
following sections of the
Ghosh and Ramamoorthi's
Book:
Chap.1: 1.2.2, 1.3
(excluding 1.3.3),
1.4
Bayesian Nonparametric
Methods (about 10
lectures): Materials will be
primarily based on the
following sections of an
upcoming book by Ghoshal
& van der Vaart:
Chap.1: 1.1.2, 1.2
Chap.2: 2.1, 2.2,
2.3, 2.4.2, 2.4.4
Chap.3: 3.1, 3.2,
3.5, 3.6
Chap.4: 4.1, 4.4,
4.5, 4.6, 4.8.1, 4.9.2
Last updated on: December 17, 2012
2 of 2 Printed by Sujit Ghosh 12/17/2012 3:43 PM