STAT 4130
Bayesian Data Analysis
Lec 01 selected
This course introduces the student to the Bayesian methods in applied statistics. The course covers all the fundamental concepts of Bayesian methods, and works from the simplest ideas (characterizations of probability; comparative inference; prior, posterior and predictive distributions) up through hierarchical modes applied to various data. Computational methods include MCMC for posterior simulation.
Sections
Lecture
Lec 01
Mon, Wed 14:35-15:55
From the syllabus
From the Fall 2023 syllabus by Ammar Sarhan. Open the PDF
Grading
20%MidtermTBA; in class
30%Final examTBA; in class
50%AssignmentsWeekly to biweekly
Textbook
- Bayesian Computation with R, 2nd edition, Jim Albert
Course rules
- Late work
- Late assignments receive a zero.
- Missed midterm
- Missing a midterm results in a zero unless there is a legitimate excuse; the instructor may schedule a makeup or exclude it from the final grade.
- Grade disputes
- Disputes are resolved by re-grading the entire assignment or exam.
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