Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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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.