Marginalized population Monte Carlo

Authors:
Monica F. Bugallo; Mingyi Hong;Petar M. Djuric
Affiliations:
Department of Electrical and Computer Engineering, Stony Brook University, NY 11794, USA;Department of Electrical and Computer Engineering, Stony Brook University, NY 11794, USA;Department of Electrical and Computer Engineering, Stony Brook University, NY 11794, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

Population Monte Carlo is a statistical method that is used for generation of samples approximately from a target distribution. The method is iterative in nature and is based on the principle of importance sampling. In this paper, we show that in problems where some of the parameters are conditionally linear on the remaining parameters, we can improve the computational efficiency of population Monte Carlo by generating samples of the nonlinear parameters only and marginalizing the linear parameters. We demonstrate the marginalized population Monte Carlo on the problem of frequency estimation of closely spaced sinusoids.