Bayesian Estimation and the Kalman Filter
نویسندگان
چکیده
In this tutorial article we give a Bayesian derivation of a basic state estimation result for discrete-time Markov process models with independent process and measurement noise and measurements not a ecting the state. We then list some properties of Gaussian random vectors and show how the Kalman ltering algorithm follows from the general state estimation result and a linear-Gaussian model de nition. We give some illustrative examples including a probabilistic Turing machine, dynamic classi cation, and tracking a moving object.
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