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
2.5-Day Short Course on
Practical Bayesian Non-Parametric and Semi-Parametric Modeling
Presenters: David Draper and Thanasis Kottas
Department of Applied Mathematics and Statistics
University of California, Santa Cruz
Wed-Fri 15-17 June 2005
(12.5 hours of material spread out over 2 1/2 days)
Location: Brigham Young University (Provo, Utah USA)
Keynote event of the
30th Annual Summer Institute of Applied Statistics
Sponsored by the
Department of Statistics, Brigham Young University
For more details please see
http://statweb.byu.edu/summerinstitute/index.php
Schedule - Summer Institute Check-In:
Wednesday, June 15, 2005 at 8:30am
Room 200 TMCB (Talmage Building)
Brigham Young University, Provo, UT
Lecture Workshop Sessions will be held in room 1170 TMCB all day
Wednesday, all day Thursday, and Friday morning. Thursday evening the
traditional BYU cookout will take place. The sessions will conclude with a
luncheon on Friday. Cost of the cookout and Friday luncheon is included in
the registration fee.
Nearest airport: Salt Lake City, Utah, USA
Rates/Registration: Advanced registration is requested (and will save you
money).
Academic Registration BY May 20, 2005 US$450
Academic Registration AFTER May 20, 2005 $600
Non-Academic Registration BY May 20, 2005 $700
Non-Academic Registration AFTER May 20, 2005 $850
Electronic registration is available at
http://statweb.byu.edu/summerinstitute/index.php
For CES and student rates, and any other information about the Summer
Institute, please get in touch with
Kathi Carter
Department of Statistics
230 TMCB
Brigham Young University
Provo, UT 84602 USA
email: [log in to unmask]
Tel: +1-801-422-4506
Fax: +1-801-422-0635
Short bios of the presenters
David Draper is 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
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 non-parametric and semi-parametric methods, with applications
mainly in medicine, health policy, education, and environmental risk
assessment. He has a particular interest in the exposition of complex
statistical methods and ideas in the context of real-world applications.
Thanasis Kottas is Assistant Professor in the Department of Applied
Mathematics and Statistics, Baskin School of Engineering, University of
California, Santa Cruz. His research is in the areas of Bayesian
non-parametric modeling and inference, mixtures models, semi-parametric
regression, spatial statistics, and survival analysis. He is interested in
application of Bayesian non-parametric methods in various fields,
including econometrics, epidemiology, and population dynamics.
Tentative schedule
Wed 15 June 2005
8.30am Registration and check-in
9.00-10.15am First morning session (DD)
Brief review of parametric Bayesian modeling
and its strengths and weaknesses.
The need for Bayesian non-parametric (BNP) and
Bayesian semi-parametric (BSP) modeling.
10.15-10.45am Break
10.45am-noon Second morning session (DD)
How BNP/BSP arises naturally from exchangeability
considerations and a desire to specify
Bayesian models in a coherent manner.
Low-technology BNP via Dirichlet-multinomial
modeling (a generalization of the
Bayesian bootstrap).
noon-1.15pm Lunch
1.15-2.30pm First afternoon session (DD)
General approaches for construction of BNP priors:
exchangeability, partitioning (Polya trees),
neutral-to-the-right priors, expansion of
finite-dimensional parametric models
2.30-3.00pm Break
3.00-4.30 Second afternoon session (TK)
Dirichlet process (DP) priors and mixtures of
DP priors: definitions, properties, and methods.
Applications to Bayesian bio-assay (dose-response)
modeling.
Thu 16 June 2005
9.00-10.15am First morning session (TK)
Dirichlet process mixture models: definitions,
examples, methods for posterior inference and
prediction
10.15-10.45am Break
10.45am-noon Second morning session (TK)
Applications of DP mixture models: density
estimation, nonparametric quantile regression,
hierarchical generalized linear models,
multivariate ordinal data analysis,
survival analysis
noon-1.15pm Lunch
1.15-2.30pm First afternoon session (TK)
Extensions of DP priors and DP mixture models:
dependent DP priors, spatial DP prior models.
Illustrations with spatial disease incidence data.
2.30-3.00pm Break
3.00-4.30 Second afternoon session (DD)
Polya tree (PT) priors and mixtures of PT priors:
definitions, properties, and methods.
A case study of BNP: using Polya trees for
risk assessment in nuclear waste disposal.
6.30pm Barbeque dinner
Fri 17 June 2005
9.00-10.15am First morning session (TK)
BNP regression and classification using Gaussian
process priors.
Illustrations with population dynamics data.
10.15-10.45am Break
10.45-11.30am Second morning session (DD, TK)
Overview and wrapup: strengths and limitations
of BNP/BSP.
noon-2pm Closing luncheon
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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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