Towards Robust State Estimation with Bayesian Networks: A New Perspective on Belief Propagation
نویسندگان
چکیده
We investigate properties of Bayesian networks (BNs) in the context of state estimation. We introduce a coarse perspective on the inference processes and use this perspective to identify conditions under which state estimation with BNs can be very robust, even if the quality of the model is very low. By making plausible assumptions we can formulate asymptotic properties of the estimation performance with respect to the network topology. In addition, we introduce techniques that support detection of potentially inaccurate inference results.
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