Bayesian Modeling, Inference and Prediction

DEPARTMENT OF
APPLIED MATHEMATICS & STATISTICS (AMS)

Bayesian Modeling, Inference and Prediction

Instructor: David Draper (draper@ams.ucsc.edu, (831) 459-1295)
Venue: one-day short course sponsored by the Boston Chapter of the American Statistical Association.
Date: Friday 10 December 2004 (8am-5.30pm).
Location: Hotel@MIT (Hunsaker Ballroom), 20 Sidney Street, Cambridge, Massachusetts USA 02139 (telephone: 617.577.0200)
Additional details on the course may be found here.
Handouts: (PDF format) (the handouts will be available, about a week before the course starts, to people who have registered; a sample handout is given below)
- Lecture notes: Preface, Tentative Syllabus/Outline, and part 1 (Background and Basics) (prints 22 pages, 124k)
- Lecture notes: part 2 (Exchangeability and Conjugate Modeling) (prints 48 pages, 1789k)
- Lecture notes: part 3 (Integer-Valued Outcomes; Poisson Modeling) (prints 32 pages, 615k)
- Lecture notes: part 4 (Continuous Outcomes; Gaussian Modeling) (prints 26 pages, 727k)
- Lecture notes: part 5 (Simulation-Based Computation) (prints 84 pages, 3569k)
- Lecture notes: part 6 (Hierarchical Models: Formulation, Selection, and Diagnostics) (prints 36 pages, 2349k)
- Lecture notes: part 7 (Bayesian Model Specification) (prints 40 pages, 1747k)
- Adding art to the rigor of statistical science (Leonhardt D (2001), New York Times; prints 6 pages, 365k)
- A comparison of Bayesian and likelihood-based methods for fitting multilevel models (Browne W, Draper D (2004), under review; prints 29 pages, 391k)
- Bayesian measures of model complexity and fit (with discussion) (Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002), JRSS-B, 64, 583-639; prints 57 pages, 709k)
Datasets and Code: (text format)
- NB10 example:
- IHGA example:

(last modified: 4 November 2004)