A hierarchical Bayesian framework for nonlinearities identification in gravitational wave detector outputs

Details A hierarchical Bayesian framework for nonlinearities identification in gravitational wave detector outputs A hierarchical Bayesian framework for nonlinearities identification in gravitational wave detector outputs

Sep 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005cqgra..22s1223a&link_type=abstract

Classical and Quantum Gravity, Volume 22, Issue 18, pp. S1223-S1232 (2005).

Computer Science

Learning

Scientific paper

In this paper, a hierarchical Bayesian learning scheme for autoregressive neural network models is shown which overcomes the problem of identifying the separate linear and nonlinear parts modelled by the network. We show how the identification can be carried out by defining suitable priors on the parameter space which help the learning algorithms to avoid undesired parameter configurations. Some applications to synthetic and real world experimental data are shown to validate the proposed methodology.

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