Computational Statistical Inference (STAT*6841)
Code and section: STAT*6841*01
Term: Winter
Details
Bayesian and likelihood methods, large sample theory, nuisance parameters, profile, conditional and marginal likelihoods, EM algorithms and other optimization methods, estimating functions, Monte Carlo methods for exploring posterior distributions and likelihoods, data augmentation, importance sampling and MCMC methods.
Syllabus
| Attachment | Size |
|---|---|
 Course Outline (Winter 2016) | 88.28 KB |
| Course Outline (Winter 2017) | 69.94 KB |
| Course Outline (Winter 2018) | 71.72 KB |
| Course Outline (Winter 2019) | 71.96 KB |
| Course Outline (Winter 2020) | 73.26 KB |
| Course Outline (Winter 2021) | 85.06 KB |
| Course Outline (Winter 2022) | 100.3 KB |
| Course Outline (Winter 2023) | 75.54 KB |
| Course Outline (Winter 2024) | 93.09 KB |