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The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process.