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Estimation and decision for observations derived from martingales: Part II

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2 Author(s)

The development of an approach for obtaining statistical inferences about nonobservable processes that influence a processy(cdot)which can be observed directly and which is assumed to be a mixture of continuous and discontinuous components is continued. The approach is based on probability-measure transformations and consists of finding the conditional probability of a nonobservable event in terms of the prior probability of that event and a functional of the observationsy(cdot). The topics studied include optimal filtering, smoothing, and prediction estimates of the nonobservable process;M-ary hypothesis testing; performance lower-bounds; and stochastic control.

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Information Theory, IEEE Transactions on  (Volume:24 ,  Issue: 1 )