Title :
System Identification with Analog and Counting Process Observations I: Hybrid Stochastic Intensity and Likelihood.
Author_Institution :
Dept of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor. vsolo@umich.edu
Abstract :
In a number of application areas there is growing interest in system identification for systems whose observation processes consist of both analog and counting process signals. But so far few system identification techniques exist for these cases and likelihood functions have so far not been available. Here we introduce a new hybrid stochastic intensity and use it to construct, for the first time, an analog-counting process likelihood.
Keywords :
Animals; Communication networks; Estimation; Neuroscience; Signal processing; State-space methods; Statistics; Stochastic processes; Stochastic systems; System identification;
Conference_Titel :
Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Print_ISBN :
0-7803-9567-0
DOI :
10.1109/CDC.2005.1582638