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Reduced-memory likelihood processing of point processes

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1 Author(s)

The problems of reduced-memory modeling and processing of regular point processes are studied. The m -memory processes and processors are defined as those whose present (incremental) behavior depends only on the present observation of counts and the stored values of the preceding m instants of occurrence. Characterization theorems for m -memory point processes and homogeneous reduced-memory point processes are obtained. Under proper optimization criteria, optimal reduced-memory "moving-window" information processors for point processes are derived. The results are applied to study reduced-memory processors for doubly stochastic Poisson processes (DSPP's) and to characterize m -memory DSPP's. Finally, a practically implementable scheme of a distribution-free l-memory processor is presented.

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Information Theory, IEEE Transactions on  (Volume:20 ,  Issue: 6 )