Syllabus

The Basis for this course is to develop an understanding of Bayesian principles and methodologies. We will develop the basis for subjective probability and illustrate why this is a necessary construct in many applications where classical notions of probability are degenerate. Computational examples will augment much of the material.

Probability does not exist. -De Finetti

Grading policies, office hours, and general information

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Course Objectives

• To  introduce the basic laws of probability, statistical inference, and the principles of data analysis through a Bayesian perspective.
• To provide a general framework for the Bayesian school of probability and motivate these concepts through example.
• To introduce modern computational tools in Bayesian statistics.
  

Logistics

• Lecture Times and Location:  Mon., Wed., Fri. 12:20 - 1:10 PM,  in Smyth 232.
• Instructor: Professor Scotland Leman,   401A Hutcheson Building,   leman(AT)vt(DOT)edu
• Final Exam date and time:   December 13 (Friday), 7:45AM-9:45pm
• Midterm:   Oct 25 (in class)
• Zoom Review Sessions (TA): 10AM Tuesdays   
• Instructor Review Sessions:Thursdays 6:00pm (~biweekly or as needed)
• Teaching Assistants:    Jaeyoung Lee
  
• TAs' Office Hours:  ZOOM Based (408 Hutcheson, 10AM Tuesdays)
  

Prerequisites

Students must have completed a graduate level inference class as well as some upper level class in regression. Knowledge of exponential family distributions is assumed as well as the basic constructs of probability theory.

Readings

The primary text is:

Peter D. Hoff  (2009). A First Course in Bayesian Statistical Methods  Springer.

This is a nice introductory book on Bayesian statistics. This text should not limit your reading from other relevant texts.

Computing

For computing, you may use any upper level language of your choosing. For instance, C/C++, Java, Matlab, R, all make for reasonable choices. This course will not levy a large computational burden, however, be prepared for some computational exercises.

Graded work

Graded work for the course will consist of problem sets, computational problems, one or two midterms, and a final exam. Your final grade will be determined as follows:

Final exam	40 %
Midterm exam	30 %
Homework problems/Pop quizes	30 %

There are no make-ups for exams, in-class or homework problems except for a medical or familial emergency or previous approval of the instructor.  See the instructor in advance of relevant due dates to discuss possible alternatives.

Cumulative numerical averages of 90 - 100 are guaranteed at least an A-.   Cumulative numerical averages of 80 - 89 are guaranteed at least a B-.   Cumulative numerical averages of 70 - 79 are guaranteed at least a C-.   Cumulative numerical averages of 60 - 69 are guaranteed at least a D-.  These ranges may be lowered, but they will not be raised (e.g., if everyone has averages in the 90s, everyone gets at least an A-).

Academic honesty

You are expected to abide by Virginia Tech's Community Standard for all work for this course.  Violations of the Standard will result in a failing final grade for this course and will be reported to the Dean of Students for adjudication.  Ignorance of what constitutes academic dishonesty is not a justifiable excuse for violations.

For the homework problems, you may work with a study group with others but must submit your own answers, unless otherwise indicated.  For exams, you are required to work alone and for only the specified time period.     

Procedures if you suspect your work has been graded incorrectly

Every effort will be made to mark your work accurately.    You should be credited with all the points you've worked hard to earn!   However, sometimes grading mistakes happen.  If you believe that an error has been made on an in-class problem or exam, return the paper to the instructor immediately, stating your claim in writing.

The following claims will be considered for re-grading:

(i)    points are not totaled correctly;
(ii)   the grader did not see a correct answer that is on your paper;
(iii)  your answer is the same as the correct answer, but in a different form (e.g., you wrote a correct answer as 1/3 and the grader was looking for .333);
(iv)  your answer to a free response question is essentially correct but stated slightly differently than the grader's interpretation.

The following claims will not be considered for re-grading:

(v)   arguments about the number of points lost;
(vi)  arguments about question wording.

Considering re-grades takes up valuable time and resources that TAs and the instructor would rather spend helping you understand material.  Please be considerate and only bring claims of type (i), (ii), (iii), or (iv) to our attention.