Applied Bayesian Statistics Course
About the course
This course is part of the doctorate program in Statistical Engineering at National University of Engineering. This course covers a review of likelihood-based inference, and main concepts for Bayesian inference such as prior , posterior, predictive and marginal distributions, as well as methods to perform Bayesian inference such as Markov chain Monter Carlo and other posterior-approximation methods.
Syllabus
The contents of the course is described here: syllabus.pdf.
Lessons
- Likelihood-based inference
- Introduction to Bayesian inference
- Priors and model checking
- Sampling algorithms