Bayesian source localization with the multivariate laplace prior

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1901-1909

  Van Gerven, Marcel ^a,b   Cseke, Botond ^a   Oostenveld, Robert ^b   Heskes, Tom ^a,b  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b RADBOUD UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

INVERSE PROBLEMS; MIXTURES;

BAYESIAN; COMPUTATIONAL BOTTLENECKS; EXPECTATION PROPAGATION; LAPLACE DISTRIBUTIONS; LAPLACE PRIORS; MARGINALS; MULTIVARIATE LAPLACE; PROPERTY; SCALE MIXTURES; SOURCE LOCALIZATION;

LAPLACE TRANSFORMS;

EID: 78049353036     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. P. R. Amestoy, T. A. Davis, and I. S. Duff. Algorithm 837: Amd, an approximate minimum degree ordering algorithm. ACM Transactions on Mathematical Software, 30(3):381-388, 2004.
2. D. F. Andrews and C. L. Mallows. Scale mixtures of normal distributions. Journal of the Royal Statistical Society, Series B, 36(1):99-102, 1974.
3. S. Baillet and L. Garnero. A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Transactions on Biomedical Engineering, 44(5):374-385, 1997.
4. J. M. Bernardo and J. F. M. Smith. Bayesian Theory. Wiley, 1994.
5. T. Eltoft, T. Kim, and T. Lee. On the multivariate Laplace distribution. IEEE Signal Processing Letters, 13(5):300-303, 2006.
6. M. I. Garrido, J. M. Kilner, K. E. Stephan, and K. J. Friston. The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120:453-463, 2009.
7. G. Golub and C. van Loan. Matrix Computations. John Hopkins University Press, Baltimore, MD, 3rd edition, 1996.
8. P. C. Hansen. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. Monographs on Mathematical Modeling and Computation. Society for Industrial Mathematics, 1987.
9. S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. NeuroImage, 42(2):726-738, 2008.
10. N. T. Longford. Classes of multivariate exponential and multivariate geometric distributions derived from Markov processes. In H. W. Block, A. R. Sampson, and T. H. Savits, editors, Topics in statistical dependence, volume 16 of IMS Lecture Notes Monograph Series, pages 359-369. IMS Business Office, Hayward, CA, 1990.
11. S. Lyu and E. P. Simoncelli. Statistical modeling of images with fields of Gaussian scale mixtures. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 945-952. MIT Press, Cambridge, MA, 2007.
12. T. Minka. Expectation propagation for approximate Bayesian inference. In J. Breese and D. Koller, editors, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 362-369. Morgan Kaufmann, 2001.
13. T. Minka. Power EP. Technical report, Microsoft Research, Cambridge, 2004.
14. G. Nolte. The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Physics in Medicine & Biology, 48(22):3637-3652, 2003.
15. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C. Cambridge University Press, 3rd edition, 2007.
16. M. W. Seeger. Bayesian inference and optimal design for the sparse linear model. Journal of Machine Learning Research, 9:759-813, 2008.
17. K. Takahashi, J. Fagan, and M. S. Chen. Formation of a sparse bus-impedance matrix and its application to short circuit study. In 8th IEEE PICA Conference, pages 63-69, Minneapolis, MN, 1973.
18. K. Uutela, M. Hämäläinen, and E. Somersalo. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage, 10:173-180, 1999.
19. D. Wipf and S. Nagarajan. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage, 44(3):947-966, 2009.