Greetings, and apologies for cross-posting.
If you could please bring the following announcement to the attention of
anyone you know who might benefit from seeing it, that would be terrific.
Many thanks and all best wishes, David Draper
One-Day Short Course on
Bayesian Modeling, Inference and Prediction
David Draper
Department of Applied Mathematics and Statistics
University of California, Santa Cruz
Thursday, 22 July 2004, 9am-5pm
(6.5 hours of material covered in an 8-hour time slot, with 15 minutes of
breaks in each of the morning and afternoon sessions and a one-hour break
for lunch)
Location: Renaissance Philadelphia Airport Hotel
500 Stevens Drive, 610-521-5900
Sponsored by the Philadelphia Chapter
of the American Statistical Association
Summary of Short Course Contents
This is an award-winning short course on Bayesian modeling, inference and
prediction, based on a series of case studies and assuming no previous
exposure to Bayesian ideas or methods.
Topics will include a review of classical, frequentist, and Bayesian
definitions of probability; sequential learning via Bayes' Theorem;
coherence as a form of internal calibration; Bayesian decision theory via
maximization of expected utility; review of frequentist modeling and
maximum-likelihood inference; exchangeability as a Bayesian concept
parallel to frequentist independence; prior, posterior, and predictive
distributions; Bayesian conjugate analysis of binary outcomes, and
comparison with frequentist modeling; integer-valued outcomes (Poisson
modeling); continuous outcomes (Gaussian modeling); multivariate unknowns
and marginal posterior distributions; introduction to simulation-based
computation, including rejection sampling and Markov chain Monte Carlo
(MCMC) methods; MCMC implementation strategies; introduction to Bayesian
hierarchical modeling; fitting and interpreting fixed- and random-effects
Poisson regression models; hierarchical modeling with latent variables as
an approach to mixture modeling; Bayesian model specification via
out-of-sample predictive validation (as a form of external calibration)
and the deviance information criterion (DIC).
The case studies will be drawn from medicine (diagnostic screening for
HIV; hospital-specific prediction of patient-level mortality rates;
hospital length of stay for premature births; a randomized controlled
trial of in-home geriatric assessment) and the physical sciences
(measurement of physical constants), but the lessons illustrated will
apply to a broad range of subject areas in the natural and social
sciences.
The course will liberally illustrate user-friendly implementations of MCMC
sampling via the freeware programs BUGS and WinBUGS.
The course is intended mainly for applied statisticians and will focus on
methods and applications rather than theory; an understanding of
probability and statistics at the level typically required for a Master's
degree in statistics will provide sufficient mathematical background for
participants.
Registration Fee: $110
$ 50 for full-time students
(Registration fee includes 200 pages of materials, lunch, and refreshments
for AM and PM breaks)
Registration Deadline: Friday, July 16, 2004
Registration: Send check payable to 93ASAP94 to Andrea Chrupcala, COL-B5,
Wyeth Research, P.O. Box 42528, 30th Street Station, Philadelphia, PA
19101 with an e-mail address or phone number so that a confirmation can be
sent.
Additional information may be found online at www.amstatphilly.org in
the June, 2004 ASAP newsletter.
Brief Biography of Instructor
David Draper is a Professor in, and Chair of, the Department of Applied
Mathematics and Statistics in the Baskin School of Engineering at the
University of California, Santa Cruz. From 2001 to 2003 he served as the
President-Elect, President, and Past President of the International
Society for Bayesian Analysis (ISBA). His research is in the areas of
Bayesian inference and prediction, model uncertainty and empirical
model-building, hierarchical modeling, Markov Chain Monte Carlo methods,
and Bayesian semi-parametric methods, with applications mainly in health
policy, education, and environmental risk assessment. When he gave an
earlier version of this short course at the Anaheim JSM in 1997 it
received the 1998 ASA Excellence in Continuing Education award, and a
short course he gave on intermediate and advanced-level topics in Bayesian
hierarchical modeling at the San Francisco JSM in 2003 received the 2004
ASA Excellence in Continuing Education award. He has received or been
nominated for major teaching awards everywhere he has taught (the
University of Chicago; the RAND Graduate School of Public Policy Studies;
the University of California, Los Angeles; the University of Bath (UK);
and the University of California, Santa Cruz). He has a particular
interest in the exposition of complex statistical methods and ideas in the
context of real-world applications.
Questions? Contact Paul Mange Johansen at [log in to unmask] or
484-344-3961.
Approximate Structure of the Short Course
9.00-9.30am: Quantification of uncertainty. Classical, frequentist, and
Bayesian definitions of probability. Subjectivity and objectivity.
Sequential learning; Bayes' Theorem. Inference (science) and
decision-making (policy and business). Bayesian decision theory;
coherence. Maximization of expected utility. Case study: Diagnostic
screening for HIV.
9.30-11.00am: Exchangeability and conjugate modeling. Probability as
quantification of uncertainty about observables. Binary outcomes. Review
of frequentist modeling and maximum-likelihood inference. Exchangeability
as a Bayesian concept parallel to frequentist independence. Prior,
posterior, and predictive distributions. Inference and prediction.
Coherence and calibration. Conjugate analysis. Comparison with frequentist
modeling. Case Study: Hospital-specific prediction of patient-level
mortality rates.
11.00-11.15am: Coffee break
11.15am-noon: Integer-valued outcomes; Poisson modeling. Case Study:
Hospital length of stay for birth of premature babies.
noon-12.30pm: Continuous outcomes; Gaussian modeling. Multivariate
unknowns; marginal posterior distributions. Case Study: Measurement of
physical constants (NB10).
12.30-1.30pm: Lunch break
1.30-3.00pm: Simulation-based computation. IID sampling; rejection
sampling. Introduction to Markov chain Monte Carlo (MCMC) methods: the
Metropolis-Hastings algorithm and Gibbs sampling. User-friendly
implementation of Gibbs and Metropolis-Hastings sampling via BUGS and
WinBUGS. MCMC implementation strategies. Case Study: the NB10 data
revisited.
3.00-4.00pm: Hierarchical models: formulation, selection, and diagnostics.
Poisson fixed-effects modeling. Additive and multiplicative treatment
effects. Expansion of a simple model that does not satisfy all diagnostic
checks, by embedding it in a richer class of models of which it's a
special case. Random-effects Poisson regression: hierarchical modeling
with latent variables as an approach to mixture modeling. Case study: a
randomized controlled trial of in-home geriatric assessment (IHGA).
4.00-4.15pm: Coffee break
4.15-5.00pm: Bayesian model specification. Predictive diagnostics. Model
selection as a decision problem. Bayesian cross-validation as an approach
to diagnostics: comparing outcomes from omitted cases with their
predictive distributions given the rest of the data. 3CV: 3-way
cross-validation. The log score as a model-selection method, and its
relationship to the deviance information criterion (DIC). Case study:
continuation of IHGA example.
(yes, this looks like a lot to cover in a single day :-), but I've given
this course a number of times to more than 500 participants, and almost
everybody seems happy with how things go)
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Professor David Draper
Chair, Department of
Applied Mathematics email [log in to unmask]
and Statistics web http://www.ams.ucsc.edu/~draper/
Baskin School of phone (+1) (831) 459 1295
Engineering fax (+1) (831) 459 4829
University of California
1156 High Street departmental web pages www.ams.ucsc.edu
Santa Cruz CA 95064 USA
Interesting quotes, number 24 in a series:
The end is in the beginning; and yet you go on.
-- Samuel Beckett
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