| dc.contributor.author | Christmas, JT | |
| dc.date.accessioned | 2016-03-30T12:44:08Z | |
| dc.date.issued | 2013-11-14 | |
| dc.description.abstract | We investigate the effects of missing observations on the
robust Bayesian model for spectral analysis introduced by
Christmas [2013]. The model assumes Student-t distributed
noise and uses an automatic relevance determination prior on
the precisions of the amplitudes of the component sinusoids
and it is not obvious what their effect will be when some of
the otherwise temporally uniformly sampled data is missing. | en_GB |
| dc.identifier.citation | 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 22-25 September 2013, Southampton, UK | en_GB |
| dc.identifier.doi | 10.1109/MLSP.2013.6661980 | |
| dc.identifier.uri | http://hdl.handle.net/10871/20872 | |
| dc.language.iso | en | en_GB |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
| dc.title | The effect of missing data on robust Bayesian spectral analysis | en_GB |
| dc.type | Conference paper | en_GB |
| dc.date.available | 2016-03-30T12:44:08Z | |
| dc.identifier.issn | 1551-2541 | |
| dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record. | en_GB |
