Conference item

Sequential Bayesian Estimation for Adaptive Classification

Abstract:: This paper proposes a robust algorithm to adapt a model for EEG signal classification using a modified Extended Kalman Filter (EKF). By applying Bayesian conjugate priors and marginalising the parameters, we can avoid the needs to estimate the covariances of the observation and hidden state noises. In addition, Laplace approximation is employed in our model to approximate non-Gaussian distributions as Gaussians. ©2008 IEEE.

Publication status:: Published

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Yoon, J., Roberts, S., Dyson, M., Can, J., & IEEE. (2008). Sequential Bayesian Estimation for Adaptive Classification. 33–37.

MLA Style

Yoon, J, et al. “Sequential Bayesian Estimation for Adaptive Classification.” 2008, pp. 33–37.

Chicago Style

Yoon, J, S Roberts, M Dyson, J Can, and IEEE. 2008. “Sequential Bayesian Estimation for Adaptive Classification.” 33–37.
Print

Access Document

Publisher copy:: 10.1109/MFI.2008.4648010

Authors

+ Yoon, J More by this author

Role:: Author

+ Roberts, S More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ Dyson, M More by this author

Role:: Author

+ Can, J More by this author

Role:: Author

+ IEEE More by this author

Role:: Author

Host title:: 2008 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS, VOLS 1 AND 2
Pages:: 33-37
Publication date:: 2008-01-01
DOI:: 10.1109/MFI.2008.4648010
ISBN:: 9781424421435

Keywords:: Nonlinear dynamics

Marginalisation

Laplace Approximation

Extended Kalman filter
Pubs id:: pubs:63747
UUID:: uuid:0620114d-e352-44b7-bd7e-e77d7bca7a6d
Local pid:: pubs:63747
Source identifiers:: 63747
Deposit date:: 2012-12-19
ARK identifier:: ark:/29072/ora_0620114de35244b7bd7ee77d7bca7a6d

Terms of use

Copyright date:: 2008

Licence:: Terms and Conditions of Use for Oxford University Research Archive

Views and Downloads

About views and downloads

If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP