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Subject:

ABS11 - Summer school on HIERARCHICAL MODELING FOR ENVIRONMENTAL PROCESSES, Bolzano, Italy

From:

Fabrizio Ruggeri <[log in to unmask]>

Reply-To:

Fabrizio Ruggeri <[log in to unmask]>

Date:

Thu, 14 Oct 2010 21:34:06 +0100

Content-Type:

text/plain

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We are pleased to annunce the new ABS summer school (organised in Italy
since 2004 by CNR IMATI (Milano) and Dept. of Economics, University of Pavia).

Guido Consonni and Fabrizio Ruggeri
ABS11 Directors

--------------------------------------------------------------------------------------------------

        *******************************
        *                    ABS11                            *
        *******************************

       Applied Bayesian Statistics School

HIERARCHICAL MODELING FOR ENVIRONMENTAL PROCESSES

   June, 20 - 24, 2011 - Bolzano/Bozen, Italy

Lecturer

Prof.  Alan Gelfand
J.B. Duke Professor of Statistics and Decision Sciences
Department of Statistical Science, Duke University
Durham, NC, USA

Programme and registration details are available at

>>>>   www.mi.imati.cnr.it/conferences/abs11.html   <<<<

Interested people are invited to contact the ABS11 Secretariat at

                     [log in to unmask]

---------------- COURSE OUTLINE ---------------------------------------

This course is intended to expose the value of hierarchical modeling within a Bayesian framework for investigating a range of problems in environmental science. In particular, we focus on stochastic modeling for such problems driven by the general hierarchical perspective, [data | process, parameters][process | parameters][parameters]. This specification is richer than it may appear, as the course will demonstrate. More importantly, it allows the model development to focus on the environmental process of interest, integrating the sources of information that are available. Primary problems of interest include assessment of environmental exposure, fusion of environmental data from different sources, and assessing environmental change and its potential impact on ecological processes.

The course will have a practical orientation, emphasizing model development, computation and inference driven by real examples. The course will begin with a brief review of Bayesian inference, hierarchical modeling and Bayesian computation. Then, since most environmental processes (including all of the ones we consider) are observed over space and over time, we present foundational material on spatial and space-time analysis including material on modeling for point referenced data, areal unit data, and point pattern data. We will also discuss multivariate processes, space-time processes, and computation for large datasets. Real examples will include (i) exposure assessment for particulate matter and ozone, (ii) data fusion using monitoring station data and computer model output for ozone, particulate matter and wet deposition of sulfates and nitrates, (iii) inference regarding environmental extremes illustrated through temperature data, (iv) relating environmental factors to species distributions and climate change to plant performance, (v) distributed lags in space-time regression with application to ozone formation. Sessions devoted to implementing model fitting and inference using Markov chain Monte Carlo methods will supplement the lectures and code for illustrative examples will be provided.

The school will make use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants. The slides and background reading material will be distributed to the students before the start of the course. 

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