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.

View at publisher

Abstract

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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 68783
Item Type: Conference Paper
Refereed: Yes
Additional Information: Book Subtitle:
13th International Conference, Natal, Brazil, August 29-31, 2012. Proceedings--Intelligent Data Engineering and Automated Learning - IDEAL 2012
DOI: 10.1007/978-3-642-32639-4_35
ISBN: 9783642326387
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page