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Activity Number: 195
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #308706
Title: Modeling Spatial Binary Fields Over Time With Dynamic Markov Random Fields
Author(s): Kenneth Wakeland*+
Companies: Iowa State University
Keywords: Markov Random Fields ; Non-Gaussian Distribution

Any number of problems in ecology and the environmental sciences, such as monitoring the presence/absence of a species, involve the observation of spatial binary random fields at a sequence of points in time. There is often insufficient information about the scientific processes involved to incorporate a deterministic component for time evolution into a model. We consider Markov random field models with binary conditional distributions that include a stochastic evolution over time based on autoregressive structure for the large-scale model component. These models retain the flexibility of static Markov random field models for representation of spatial dependence in the small-scale model component. Bayesian estimation is accomplished through the use of what has been called the 'double Metropolis algorithm' which requires generation of auxiliary random fields, but does not require the use of perfect sampling. Use of the model is illustrated with simulated and real data.

Authors who are presenting talks have a * after their name.

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