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Model order estimation of 2D autoregressive processes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The work on model-order estimation by Bayesian predictive densities of 1-D real autoregressive processes is extended to 2-D complex autoregressive processes. According to the procedure, the best model is the one which most accurately predicts the data yet to be observed and whose parameters are estimated from the data already observed. The derivation steps of the algorithm are demonstrated and verified by computer simulations. The computer simulations show that the algorithm based on this approach yields good results.

Original languageEnglish
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherPubl by IEEE
Pages3405-3408
Number of pages4
ISBN (Print)078030033
StatePublished - 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: May 14 1991May 17 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume5

Conference

ConferenceProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period05/14/9105/17/91

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