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Bayesian Hierarchical Spatial Models with Applications

Applications of Markov Chain Monte Carlo (MCMC) methods have become popular in fitting hierarchical spatial models from a Bayesian point of view. I will demonstrate their usefulness by discussing two spatial applications. The first one involves the analysis of a transport mode choice study conducted in Munich, Germany, while the second is modeling the total claim size in a German car insurance portfolio. This work is joint with S. Prokopenko and S. Gschloessl.