Abstract
This paper presents a sequential state estimation method with arbitrary probabilistic models expressing the system’s belief. Probabilistic models can be estimated by Maximum a posteriori estimators (MAP), which fail, if the state is dynamic or the model contains hidden variables. The last typically requires iterative methods like expectation maximization (EM). The proposed approximative technique extends message passing algorithms in factor graphs to realize online state estimation despite of hidden parameters. In addition no conjugate priors or hyperparameter transition models have to be specified. For evaluation, we show the relation to EM and discuss the transition model in detail.
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References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer Science+Business Media, Secaucus, NJ, USA (2006)
Lauritzen, S.L.: Graphical Models. Clarendon Press, Oxford (1996)
Jordan, M.I., Sejnowski, T.J.: Graphical Models: Foundations of Neural Computation. MIT Press, Cambridge (2001)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. UC Berkeley (2002)
Kschischang, F.R., Frey, B.J., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47, 498–519 (2001)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo (1988)
Frey, B.J., Kschischang, F.R., Loeliger, H.A., Wiberg, N.: Factor graphs and algorithms. In: Proceedings of the 35th ACOCCC 1998, pp. 666–680 (1997)
Diaconis, P., Ylvisaker, D.: Conjugate priors for exponential families. Annals of Statistics 7, 269–281 (1979)
Dempster, A.P., Laird, N.M., Rubin, D.B., et al.: Maximum likelihood from incomplete data via the EM algorithm. JRSS 39, 1–38 (1977)
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© 2009 Springer-Verlag Berlin Heidelberg
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Volkhardt, M., Kalesse, S., Müller, S., Gross, HM. (2009). Maximum a Posteriori Estimation of Dynamically Changing Distributions. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_61
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DOI: https://doi.org/10.1007/978-3-642-04617-9_61
Publisher Name: Springer, Berlin, Heidelberg
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