Bayesian regression mixtures of experts for geo-referenced data

Politicians planners and social scientists have an increas ing need for tools clarifying the spatial distribution of relevant features Special interest is in what if analyses what would happen if we change some features in a speci c 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 repre sent 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