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        <identifier>oai.jstatsoft/v03/i07</identifier>
        <datestamp>2012-08-28</datestamp>
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            <dc:title>Using INLA To Fit A Complex Point Process Model With Temporally Varying Effects &#8211; A Case Study</dc:title>
            <dc:creator>Janine B&#228;rbel Illian, Sigrunn Holbek S&#248;rbye, H&#229;vard Rue, Ditte Katrine Hendrichsen</dc:creator>
            <dc:date>2012-08-28</dc:date>
            <dc:publisher>Journal of Environmental Statistics</dc:publisher>
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              <![CDATA[Vol. 3, Issue 7, Aug 2012
Abstract:
Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We illustrate the approach by fitting a model to data on the locations of muskoxen (Ovibos moschatus) herds in Zackenberg valley, Northeast Greenland and by detailing how this model is specified within the R-interface R-INLA. The paper strongly focuses on practical problems involved in the modelling process, including issues of spatial scale, edge effects and prior choices, and finishes with a discussion on models with varying boundary conditions.]]>
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            <dc:subject>Statistics</dc:subject>
            <dc:subject>Environment</dc:subject>
            <dc:identifier>http://www.jenvstat.org/v03/i07</dc:identifier>
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