Bayesian regression mixtures of experts for geo-referenced data

Article type: Research Article

Authors: Paaß, Gerhard | Kindermann, Jörg

Affiliations: Fraunhofer Institute for Autonomous Intelligent Systems (AIS), 53754 St. Augustin, Germany. E-mail: [email protected], [email protected]

Abstract: Politicians, planners and social scientists have an increasing need for tools clarifying the spatial distribution of relevant features. Special interest is in predicting changes in a what-if analysis: what would happen if we change some features in a specific way. To predict future developments requires a statistical model with inherent modelling uncertainty. In this paper we investigate Bayesian models which on the one hand are able to represent complex relations between geo-referenced variables and on the other hand estimate the inherent uncertainty in predictions. For solution the models require Markov-Chain Monte Carlo techniques.

DOI: 10.3233/IDA-2003-7605

Journal: Intelligent Data Analysis, vol. 7, no. 6, pp. 567-582, 2003