Title :
Model selection in continuous time
Author :
Gerencsér, László ; Vágó, Zsuzsanna
Author_Institution :
Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
Abstract :
The foundations of a theory of model selection for continuous-time autoregressive systems is outlined. The authors define the predictive stochastic complexity for continuous-time systems and investigate its asymptotic properties. An almost sure asymptotic result is presented
Keywords :
modelling; stochastic processes; stochastic systems; asymptotic properties; continuous-time autoregressive systems; model selection; predictive stochastic complexity; Codes; Continuous time systems; Control systems; Mathematics; Mechanical engineering; Parameter estimation; Recursive estimation; Robot control; Stochastic processes; Stochastic systems;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261466
