JTW > Courses > STAT 489: Bayesian Methods of Data Analysis
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Vital Statistics

Lecture Room

GOS(08)-1300

Class Times

Tuesday & Thursday, 14:00-15:15

Official Textbooks

• Gelman, Carlin, Stern, Dunson, Vehtari & Rubin, Bayesian Data Analysis, Third Edition (CRC, 2013). ISBN 978-1-4398-4095-5
• McElreath, Statistical Rethinking: A Bayesian Course with Examples in R and Stan, First Edition (CRC, 2017). ISBN 978-1-4822-5344-3

Recommended Textbooks

• Bolstad & Curran, Introduction to Bayesian Statistics, Third Edition (Wiley, 2016). ISBN 978-1-1180-9156-2
• Bolstad, Understanding Computational Bayesian Statistics, First Edition (Wiley, 2009). ISBN 978-0-4700-4609-8
• Sivia with Skilling, Data analysis: a Bayesian tutorial, Second Edition (Oxford, 2006). ISBN 978-0-1985-6832-2
  Also available as an electronic book through the Wallace Library.
• Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, JAGS and Stan, Second Edition (Academic Press, 2014). ISBN 978-0-1240-5888-0

Other Useful References

• Jaynes, Probability Theory: The Logic of Science, First Edition (Cambridge, 2003). ISBN 978-0-52-159271-0

Course Webpage

http://ccrg.rit.edu/~whelan/STAT-489/ (this page)

Instructor

Dr. John T. Whelan

LAC(74)-2063, 5-5083

john.whelan@astro.rit.edu or jtwsma@rit.edu

Office Hours (Tentative)

MT 10:00-11:30

Course outline

Syllabus

Mailing list

discuss-bayesian-data-analysis

Topics and Course Calendar

We will cover a range of topics in Bayesian Inference and Data Analysis, likely to include:

• Bayesian interpretation of probability
• Bayesian parameter estimation
• Bayesian hypothesis testing
• Numerical methods, including Markov chain Monte Carlo

A tentative timetable is given in the Course Calendar.

Lecture Notes

• Part One: Bayesian Parameter Estimation
  • R commands from demonstrations
• Part Two: Bayesian Model Evaluation
  • R commands from demonstrations
• Part Three: Computational Methods
  • R commands from demonstrations
• Part Four: Generalized Linear Models
  • R commands from demonstrations
• Bayesian Statistics Notes from STAT 406

Problem Sets

• Problem Set 1 -- Due 2017 January 31
• Problem Set 2 -- Due 2017 February 9
• Problem Set 3 -- Due 2017 February 14
• Problem Set 4 -- Due 2017 February 21
• Problem Set 5 -- Due 2017 March 9
  • data for problem 2
• Problem Set 6 -- Due 2017 March 23
• Problem Set 7 -- Due 2017 March 30
• Problem Set 8 -- Due 2017 April 6
  • JAGS model for problem 2
• Problem Set 8½ -- Due 2017 April 13
• Problem Set 9 -- Due 2017 April 27
• Problem Set 10 -- Due 2017 May 4
• Problem Set 11 -- Due 2017 May 11

Webpages for Other Courses

I've taught some other courses which contained Bayesian material.

• STAT 406: Mathematical Statistics II (Spring 2016)
• ASTP 611: Statistical Methods for Astrophysics (Spring 2014)

Links

• R (computing language)
  • RStudio
  • rethinking R library
• Gelman, Bayesian Data Analysis
• McElreath, Statistical Rethinking
  • Lecture slides
  • rethinking R library
• Kruschke, Doing Bayesian Data Analysis