December 2009 Archives

Off the grid

For the first time since I've started using email on a regular basis, I'll be without it for the next week as I spend quality time with the family. For that matter, it'll be my first time in a while without pointing my eyes toward a screen. If you're reading this between December 20 and 27, you're clearly not doing the same thing.

Tentative syllabus for 36-724: Applied Bayesian Statistical Computing

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
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.

New Journal: Statistics, Politics and Policy

For those of us who would like to see more concrete discussion of policy issues with a strong numerical component, the journal Statistics, Politics and Policy is currently in pre-launch for a first issue this coming summer. Full disclosure: I am also serving as an associate editor, but I wouldn't have joined if I didn't believe it would be high-quality.

Because SPP is published online by Berkeley Electronic Press, it promises to have a quick turnaround time for submissions as well as great accessibility. And it's definitely something I'll try to uphold in my role with the journal.

Citation Software My High Standards Won't Accept

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The main reason I started to develop PaperTrail was that no other software out there was suitable for literature reviews and identifying commonly cited papers. I've been pointed towards several other options that don't do it.

Cross-platform: Zotero as a browser app isn't comprehensive enough. Mendeley is too much in the power of another company and isn't open source. JabRef is a fair product which I'd probably use if not for the lack of cross-citations.

Mac software: Papers and BibDesk are both citation/paper managers but are OS X only. Papers isn't free. Plus I don't have a Mac.