ST810 – Valid Probabilistic Inference

Updated 09/19/2016
 Name: Ryan Martin
 Office: 5238 SAS Hall
 Phone: 9195151920
 Email: rgmarti3 AT ncsu.edu (best way to contact me)
 Syllabus: PDF file
 Lecture: T 3:00–5:45pm in SAS 5270
 Office hrs: Wednesday 1–2pm, or by appointment.
 Textbook: R. Martin and C. Liu, Inferential Models, Chapman & Hall/CRC Press, 2015.
 Software: R is free to download at http://cran.rproject.org/
Please check this section occasionally for updates to the schedule or other information.
09/19: Here are the codes for the binomial IM example in class.
09/04: Sorry for the delay, but here is the description of the course project that I promised you. There are quite a few ideas of possible projects given there, but this certainly isn't exhaustive, so feel free to talk with me about any other ideas you might have. I'll say a few words about the project at the beginning of class on 09/05.
08/29: Here are the codes for the confidence distribution marginalization example in class.
08/17: I am going to assume that students are familiar with the basics of probability and statistical theory, as covered in ST521 and ST522 (now ST701 and ST702). More specifically, the ideas presented in my lecture notes here and especially here are the kinds of things I expect that you are familiar with. It's not necessary that you be familiar with the measuretheoretic stuff in the latter set of notes, but it wouldn't hurt. Some background with Bayesian methods and computational statistics (e.g., simulation, MCMC, optimization, etc) would also be helpful, though not required.
08/17: As described in the syllabus, pass/fail grades will be assigned based largely on a final course project. Details about the project, along with some suggested topics, will be given soon.
08/17: Welcome to ST810!
What is statistical inference?
Existing approaches.
Inferential models—the basics.
Some details on random sets.
Improved efficiency via dimension reduction.
Applications.
Extensions and open problems.