Classical and Bayesian Inference

This course is an introduction to Statistical Inference at a calculus-based level. The topics covered are: Bayesian Inference; maximum likelihood estimation; sufficient statistics; distribution of estimators; confidence intervals; hypothesis testing.



We will follow DeGroot, M. H. and Schervish, M. J. Probability and Statistics (4th Edition), Addison Wesley (DGS). We will focus on chapters 6 to 9. Chapters 1 to 5 are covered in AMS 131. For the second part of the course (after week 5), readings will also be taken from: Gelman, A., Carlin, JB., Stern HS, Rubin, DB (2004), Bayesian data analysis (2nd Edition). New York: Chapman & Hall/CRC.


Instructor Office Hours:

Tuesdays and Thursdays from 10:00 am to 11:00 am, BE 357B

Wednesdays from 11:00 am to 12:30 pm, BE 357B

TA Office Hours:

Mondays 9:00 am to 10:00 am; Thursdays 11:00 am to 12:00 m, Jack´s Lounge, BE 1st Floor

Instructors and Assistants