Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC
Rohde, David, Corcoran, Jonathan, White, Gentry, & Huang, Ruth (2012) Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC. In Intelligent Data Engineering and Automated Learning - IDEAL 2012, August 29-31, 2012, Natal, Brazil.
An important aspect of decision support systems involves applying sophisticated and flexible statistical models to real datasets and communicating these results to decision makers in interpretable ways. An important class of problem is the modelling of incidence such as fire, disease etc. Models of incidence known as point processes or Cox processes are particularly challenging as they are ‘doubly stochastic’ i.e. obtaining the probability mass function of incidents requires two integrals to be evaluated. Existing approaches to the problem either use simple models that obtain predictions using plug-in point estimates and do not distinguish between Cox processes and density estimation but do use sophisticated 3D visualization for interpretation. Alternatively other work employs sophisticated non-parametric Bayesian Cox process models, but do not use visualization to render interpretable complex spatial temporal forecasts. The contribution here is to fill this gap by inferring predictive distributions of Gaussian-log Cox processes and rendering them using state of the art 3D visualization techniques. This requires performing inference on an approximation of the model on a discretized grid of large scale and adapting an existing spatial-diurnal kernel to the log Gaussian Cox process context.
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|Item Type:||Conference Paper|
|Additional Information:||Book Subtitle:
13th International Conference, Natal, Brazil, August 29-31, 2012. Proceedings--Intelligent Data Engineering and Automated Learning - IDEAL 2012
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
|Divisions:||Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||20 May 2014 00:06|
|Last Modified:||16 Jul 2014 05:08|
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