Visualization of predictive distributions for discrete spatial-temporal log cox processes approximated with MCMC

Rohde, David, Corcoran, Jonathan, White, Gentry and Huang, Ruth (2012). Visualization of predictive distributions for discrete spatial-temporal log cox processes approximated with MCMC. In: Hujun Yin, José A. F. Costa and Guilherme Barreto, Intelligent Data Engineering and Automated Learning - IDEAL 2012: 13th International Conference, proceedings. 13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2012), Natal, Brazil, (286-293). 29 - 31 August 2012.


Author Rohde, David
Corcoran, Jonathan
White, Gentry
Huang, Ruth
Title of paper Visualization of predictive distributions for discrete spatial-temporal log cox processes approximated with MCMC
Conference Paper Type Fully Published Paper
Conference name 13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2012)    (ERA 2010 Rank C)
Conference location Natal, Brazil
Conference dates 29 - 31 August 2012
Proceedings title Intelligent Data Engineering and Automated Learning - IDEAL 2012: 13th International Conference, proceedings  (ERA 2012 Listed)   Check publisher's open access policy
Journal name Lecture Notes in Computer Science  (ERA 2012 Listed)   Check publisher's open access policy
Editor Hujun Yin
José A. F. Costa
Guilherme Barreto
Place published Heidelberg, Germany
Publisher Springer
Publication date 2012
Volume number 7435
ISBN 9783642326387; 9783642326394
ISSN 0302-9743; 1611-3349
Start page 286
End page 293
Total pages 8
Collection year 2013
Language eng
Abstract/Summary 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.
Keyword Non-parametric Bayesian inference
Markov chain Monte Carlo
Visualization
Spatial-temporal Cox processes
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 26 Sep 2012, 09:19:56 EST by Ms Imogen Ferrier on behalf of School of Geography, Planning & Env Management