The Reference Prior for Complex Covariance Matrices With Efficient Implementation Strategies
Journal article, 2010

The paper derives the reference prior for complex covariance matrices. The reference prior is a noninformative prior that circumvents some of the weaknesses of common alternatives in multidimensional settings. As a consequence, inference based on this prior renders well-behaving solutions that in many cases outperform traditionally used approaches. The main obstacle is that inference based on this prior require integration over high-dimensional spaces which have no closed form solutions. A focus of the paper is therefore to discuss efficient implementation strategies based on Markov chain Monte Carlo methods. It is identified that certain structures can be treated analytically both for the case where the parameter of interest is the covariance matrix itself but also for cases in which the covariance matrix is a nuisance parameter that characterizes noise color. Evaluation in both these settings also verify the superior performance obtained by using the proposed prior as compared to traditional techniques to treat unknown covariance matrices.

Adaptive estimation

reference prior

covariance matrix

MCMC

Author

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Magnus Nordenvaad

Luleå University of Technology

Swedish Defence Research Agency (FOI)

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 58 1 53-66 5165045

Subject Categories

Probability Theory and Statistics

DOI

10.1109/TSP.2009.2027768

More information

Latest update

5/14/2018