Bayesian regression mixtures of experts for geo-referenced data

Authors:
Gerhard Paaß;Jörg Kindermann
Affiliations:
Fraunhofer Institute for Autonomous Intelligent Systems (AIS), 53754 St. Augustin, Germany. E-mail: {Paass,Kindermann}@ais.fraunhofer.de;Fraunhofer Institute for Autonomous Intelligent Systems (AIS), 53754 St. Augustin, Germany. E-mail: {Paass,Kindermann}@ais.fraunhofer.de
Venue:
Intelligent Data Analysis
Year:
2003

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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.