The effect of missing data on robust Bayesian spectral analysis

Abstract

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Published in: Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on Date of Conference: 22-25 Sept. 2013We 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