As previously offered, this course was a full semester 12 unit course following three semester courses in mathematical statistics, regression modelling and computation. Now, as there is room only for six weeks and no precursor course in computing, I'm still working on how to pick the essential concepts and put them into a seven-week course. Here's what I've got so far.
Carnegie Mellon University, Spring 2010: 36-724: Applied Bayesian Statistical Computing
Instructor: Andrew C. Thomas (acthomas at stat.cmu.edu)
Class Time/Place: MWF 11:30-12:20, CFA 211
Required text:
Andrew Gelman and Jennifer Hill (2007) "Data Analysis using Regression and Multilevel/Hierarchical Models". Cambridge University Press. Buy the softcover version.
Prerequisites: 36-705 ``Intermediate Statistics'', 36-707 ``Intermediate Regression''. If you have not taken these classes specifically, examine the syllabuses for these courses and make an appointment to see me within the first week of class.
The goal of this course is to give a meaningful introduction and exploration of Bayesian statistical methods through computational techniques in seven weeks. We will focus on the principles of Bayesian hierarchical modelling methods that can be programmed efficiently and remain scientifically valid, and methods for debugging without pulling too much hair out. We will not be explicitly covering discriminative machine-learning topics, but we will cover the same debugging concepts that will make things easier when coding them up.
Programming language: R will be the supported language for the course, with the possible use of WinBUGS.
Tentative outline of the course:
Week 1: Introductions. "Central Dogma of Generative Modelling", One-level models, prior specifications and conjugacy; introduction to sampling and simulation in R.
Week 2: A reintroduction to Markov Chain theory, beginning with discrete models and moving to one-dimensional continuous models.
Week 3: Generalized linear models. Grid sampling, the Metropolis-Hastings algorithm, Gibbs sampling.
Week 4: Gaussian multilevel models. Partial and full pooling of variance components; autocorrelation and cross-correlation in chains; diagnostics for convergance.
Week 5: Generalized multilevel models; posterior predictive checking.
Week 6: Varying-slope models in the multilevel context.
Week 7: Special topics to be determined; Bayesian graphical models, causal inference.
If you have any suggestions for topics that ought to be considered, please let me know.
Carnegie Mellon University, Spring 2010: 36-724: Applied Bayesian Statistical Computing
Instructor: Andrew C. Thomas (acthomas at stat.cmu.edu)
Class Time/Place: MWF 11:30-12:20, CFA 211
Required text:
Andrew Gelman and Jennifer Hill (2007) "Data Analysis using Regression and Multilevel/Hierarchical Models". Cambridge University Press. Buy the softcover version.
Prerequisites: 36-705 ``Intermediate Statistics'', 36-707 ``Intermediate Regression''. If you have not taken these classes specifically, examine the syllabuses for these courses and make an appointment to see me within the first week of class.
The goal of this course is to give a meaningful introduction and exploration of Bayesian statistical methods through computational techniques in seven weeks. We will focus on the principles of Bayesian hierarchical modelling methods that can be programmed efficiently and remain scientifically valid, and methods for debugging without pulling too much hair out. We will not be explicitly covering discriminative machine-learning topics, but we will cover the same debugging concepts that will make things easier when coding them up.
Programming language: R will be the supported language for the course, with the possible use of WinBUGS.
Tentative outline of the course:
Week 1: Introductions. "Central Dogma of Generative Modelling", One-level models, prior specifications and conjugacy; introduction to sampling and simulation in R.
Week 2: A reintroduction to Markov Chain theory, beginning with discrete models and moving to one-dimensional continuous models.
Week 3: Generalized linear models. Grid sampling, the Metropolis-Hastings algorithm, Gibbs sampling.
Week 4: Gaussian multilevel models. Partial and full pooling of variance components; autocorrelation and cross-correlation in chains; diagnostics for convergance.
Week 5: Generalized multilevel models; posterior predictive checking.
Week 6: Varying-slope models in the multilevel context.
Week 7: Special topics to be determined; Bayesian graphical models, causal inference.
If you have any suggestions for topics that ought to be considered, please let me know.