Switching Kalman Filters
نویسنده
چکیده
We show how many di erent variants of Switching Kalman Filter models can be represented in a uni ed way, leading to a single, general-purpose inference algorithm. We then show how to nd approximate Maximum Likelihood Estimates of the parameters using the EM algorithm, extending previous results on learning using EM in the non-switching case [DRO93, GH96a] and in the switching, but fully observed, case [Ham90].
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تاریخ انتشار 1998