JSM 2004 Online Program

Abstract #300210

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Activity Number:	5
Type:	Invited
Date/Time:	Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #300210
Title:	Hierarchical Bayesian Models for Spatio-temporal Processes in Ecology
Author(s):	Christopher K. Wikle*+
Companies:	University of Missouri, Columbia
Address:	Dept. of Statistics, Columbia, MO, 65211,
Keywords:	spatial ; partial differential equations
Abstract:	Ecological processes often encompass a very extensive range of spatial and temporal scales of variability, and include complicated interactions across domains, variables, and systems. To understand and eventually predict such complicated processes, we must make use of available scientific knowledge, as well as honestly account for uncertainties in that knowledge. Furthermore, we must account for the uncertainty in our observations. Hierarchical Bayesian spatio-temporal models can accommodate these uncertainties and provide rich models for ecological processes. In particular, we will discuss such models motivated by partial differential equation priors.

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