Teaching
- DSA 595 Bayesian computations for machine learning. Spring 2025.
Link: Course website.
- ST 790 Navigating the PhD program and beyond: perspectives, skills, and strategies. Fall 2024.
Link: Course website.
- ST 453 Advanced computing for statistical reasoning. Fall 2024.
Link: Course website.
- ST 421 Introduction to mathematical statistics I. Fall 2024.
Link: Course website.
- ST 705 Linear models and variance components. Spring 2025.
Link: Course website.
- ST 371 Introduction to probability and distribution theory. Fall 2020.
Link: Course website.
- ST 502 Fundamentals of statistical inference II. Fall 2019.
Link: Course website.
When the mathematician expresses frustration that something in mathematics is hard to understand, they are really admitting a lack of capacity for understanding what the story is. When the student expresses frustration in learning mathematics, it is because they have not yet understood that a story exists.
Resources
- Markov chain Monte Carlo simple example. As part of a short introduction to MCMC during a presentation, I wrote a simple MCMC example. I now find myself going back to these slides when I help people who are learning about the Metropolis-Hastings algorithm for the first time. The slides are available HERE, and the R source code is avaiable HERE. A good exercise for those who are new to MCMC is to recreate the example in R (or whatever language), referencing the R source code as needed.