-
1
-
-
0031288562
-
-
A. Mohammad-Djafari, A Bayesian estimation method for detection, localisation and estimation of superposed sources in remote sensing, in: SPIE'97, San Diego, July 1997
-
-
-
-
2
-
-
34547464517
-
-
A. Mohammad-Djafari, A Bayesian approach to source separation, in: Proc. 19th International Workshop on Bayesian Inference and Maximum Entropy Methods (MaxEnt99), Boise, USA, August 1999
-
-
-
-
3
-
-
57649118678
-
-
K.H. Knuth, Bayesian source separation and localization, in: SPIE'98: Bayesian Inference for Inverse Problems, San Diego, July 1998, pp. 147-158
-
-
-
-
4
-
-
34547420030
-
-
K.H. Knuth, A Bayesian approach to source separation, in: Proc. 1st International Workshop on Independent Component Analysis and Signal Separation, Aussois, France, January 1999, pp. 283-288
-
-
-
-
5
-
-
34547442273
-
-
K.H. Knuth, H.G. Vaughan, Convergent Bayesian formulations of blind source separation and electromagnetic source estimation, in: Maximum Entropy and Bayesian Methods (MaxEnt), Munich, 1998, pp. 217-226
-
-
-
-
6
-
-
22944434926
-
A Bayesian approach to blind source separation
-
Rowe D.B. A Bayesian approach to blind source separation. J. Interdisciplin. Math. 5 1 (2002) 49-76
-
(2002)
J. Interdisciplin. Math.
, vol.5
, Issue.1
, pp. 49-76
-
-
Rowe, D.B.1
-
9
-
-
0002741125
-
Ensemble learning for blind source separation
-
Roberts S.J., and Everson R.M. (Eds), Cambridge Univ. Press, Cambridge
-
Miskin J., and Mackay D. Ensemble learning for blind source separation. In: Roberts S.J., and Everson R.M. (Eds). Independent Component Analysis (2001), Cambridge Univ. Press, Cambridge 209-233
-
(2001)
Independent Component Analysis
, pp. 209-233
-
-
Miskin, J.1
Mackay, D.2
-
12
-
-
35048903602
-
-
C. Févotte, S.J. Godsill, P.J. Wolfe, Bayesian approach for blind separation of underdetermined mixtures of sparse sources, in: Proc. 5th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2004), Granada, Spain, 2004, pp. 398-405
-
-
-
-
13
-
-
34547429873
-
-
C. Févotte, S.J. Godsill, A Bayesian approach for blind separation of sparse sources, IEEE Trans. Speech and Audio Processing, in press, available at http://persos.mist-technologies.com/~cfevotte/
-
-
-
-
14
-
-
1242316819
-
Blind source separation by sparse decomposition
-
Roberts S.J., and Everson R.M. (Eds), Cambridge Univ. Press, Cambridge
-
Zibulevsky M., Pearlmutter B.A., Bofill P., and Kisilev P. Blind source separation by sparse decomposition. In: Roberts S.J., and Everson R.M. (Eds). Independent Component Analysis: Principles and Practice (2001), Cambridge Univ. Press, Cambridge
-
(2001)
Independent Component Analysis: Principles and Practice
-
-
Zibulevsky, M.1
Pearlmutter, B.A.2
Bofill, P.3
Kisilev, P.4
-
15
-
-
0033692661
-
-
A. Jourjine, S. Rickard, O. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing n sources from 2 mixtures, in: Proc. ICASSP-5, Istanbul, Turkey, June 2000, pp. 2985-2988
-
-
-
-
17
-
-
34547412386
-
-
M. Wainwright, M.I. Jordan, Graphical models, exponential families, and variational inference, Technical Report 649, Department of Statistics, UC Berkeley, September 2003
-
-
-
-
19
-
-
0033561886
-
Independent factor analysis
-
Attias H. Independent factor analysis. Neural Comput. 11 4 (1999) 803-851
-
(1999)
Neural Comput.
, vol.11
, Issue.4
, pp. 803-851
-
-
Attias, H.1
-
20
-
-
34547431437
-
-
H. Lappalainen, Ensemble learning for independent component analysis, in: Proceedings of Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), Aussois, France, 1999, pp. 7-12
-
-
-
-
21
-
-
34547420524
-
-
H. Valpola, Nonlinear independent component analysis using ensemble learning: Theory, in: Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, ICA 2000, Helsinki, Finland, 2000, pp. 251-256
-
-
-
-
22
-
-
0038132749
-
A variational method for learning sparse and overcomplete representations
-
Girolami M. A variational method for learning sparse and overcomplete representations. Neural Comput. 13 11 (2001) 2517-2532
-
(2001)
Neural Comput.
, vol.13
, Issue.11
, pp. 2517-2532
-
-
Girolami, M.1
-
23
-
-
0001387715
-
Mean-field approaches to independent component analysis
-
Hojen-Sorensen P., Winther O., and Hansen L.K. Mean-field approaches to independent component analysis. Neural Comput. 14 (2002) 889-918
-
(2002)
Neural Comput.
, vol.14
, pp. 889-918
-
-
Hojen-Sorensen, P.1
Winther, O.2
Hansen, L.K.3
-
24
-
-
0041324801
-
Variational Bayesian learning of ica with missing data
-
Chan K., Lee T.W., and Sejnowski T.J. Variational Bayesian learning of ica with missing data. Neural Comput. 15 (2003) 1991-2011
-
(2003)
Neural Comput.
, vol.15
, pp. 1991-2011
-
-
Chan, K.1
Lee, T.W.2
Sejnowski, T.J.3
-
25
-
-
34547409705
-
-
O. Winther, K.B. Petersen, Flexible and efficient implementations of Bayesian independent component analysis, Neurocomputing, 2006, submitted for publication
-
-
-
-
27
-
-
84863650825
-
-
A.T. Cemgil, C. Fevotte, S.J. Godsill, Blind separation of sparse sources using variational EM, in: 13th European Signal Processing Conference, Antalya, Turkey, 2005. EURASIP. URL http://www-sigproc.eng.cam.ac.uk/~cf269/eusipco05/sound_files.html
-
-
-
-
28
-
-
34547481747
-
-
S. Moussaoui, D. Brie, A. Mohammad-Djafari, C. Carteret, Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling, IEEE Trans. Signal Process., in press
-
-
-
-
29
-
-
0003860037
-
-
Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds), CRC Press, London
-
In: Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds). Markov Chain Monte Carlo in Practice (1996), CRC Press, London
-
(1996)
Markov Chain Monte Carlo in Practice
-
-
-
30
-
-
5744249209
-
Equations of state calculations by fast computing machines
-
Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., and Teller E. Equations of state calculations by fast computing machines. J. Chem. Phys. 21 (1953) 1087-1091
-
(1953)
J. Chem. Phys.
, vol.21
, pp. 1087-1091
-
-
Metropolis, N.1
Rosenbluth, A.2
Rosenbluth, M.3
Teller, A.4
Teller, E.5
-
31
-
-
77956890234
-
Monte Carlo sampling methods using Markov chains and their applications
-
Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
-
(1970)
Biometrika
, vol.57
, pp. 97-109
-
-
Hastings, W.K.1
-
32
-
-
0040944354
-
Markov chain Monte Carlo: Some practical implications of theoretical results
-
Roberts G.O., and Rosenthal J.S. Markov chain Monte Carlo: Some practical implications of theoretical results. Can. J. Statist. 26 (1998) 5-31
-
(1998)
Can. J. Statist.
, vol.26
, pp. 5-31
-
-
Roberts, G.O.1
Rosenthal, J.S.2
-
33
-
-
34547463449
-
-
W. Wiegerinck, Variational approximations between mean field theory and the junction tree algorithm, in: UAI (16th conference), 2000, pp. 626-633
-
-
-
-
35
-
-
0002788893
-
A view of the EM algorithm that justifies incremental, sparse, and other variants
-
MIT Press, Cambridge, MA. 0-262-60032-3
-
Neal R.M., and Hinton G.E. A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models (1999), MIT Press, Cambridge, MA. 0-262-60032-3 355-368
-
(1999)
Learning in Graphical Models
, pp. 355-368
-
-
Neal, R.M.1
Hinton, G.E.2
-
37
-
-
25444480219
-
On the effect of the form of the posterior approximation in variational learning of ica models
-
Ilin A., and Valpola H. On the effect of the form of the posterior approximation in variational learning of ica models. Neural Process. Lett. 22 2 (2005)
-
(2005)
Neural Process. Lett.
, vol.22
, Issue.2
-
-
Ilin, A.1
Valpola, H.2
-
38
-
-
84899024135
-
-
D. Barber, W. Wiegerinck, Tractable variational structures for approximating graphical models, in: M. Kearns, S. Solla, D. Cohn (Eds.), Advances in Neural Information Processing Systems (NIPS), 1999, pp. 183-189
-
-
-
-
41
-
-
34547412385
-
-
L.K. Hansen, K.B. Petersen, Monaural ICA of white noise mixtures is hard, in: Proceedings of ICA 2003, pp. 815-820
-
-
-
-
42
-
-
22944474285
-
On the slow convergence of EM and VBEM in low noise linear mixtures
-
Petersen K.B., Winther O., and Hansen L.K. On the slow convergence of EM and VBEM in low noise linear mixtures. Neural Comput. 17 (2005) 1-6
-
(2005)
Neural Comput.
, vol.17
, pp. 1-6
-
-
Petersen, K.B.1
Winther, O.2
Hansen, L.K.3
-
43
-
-
0001692404
-
Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation
-
Jordan M.I. (Ed), Kluwer Academic, Dordrecht
-
Neal R.M. Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation. In: Jordan M.I. (Ed). Learning in Graphical Models (1998), Kluwer Academic, Dordrecht 205-225
-
(1998)
Learning in Graphical Models
, pp. 205-225
-
-
Neal, R.M.1
-
44
-
-
0001460136
-
On sequential Monte Carlo sampling methods for Bayesian filtering
-
Doucet A., Godsill S., and Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Comput. 10 3 (2000) 197-208
-
(2000)
Statist. Comput.
, vol.10
, Issue.3
, pp. 197-208
-
-
Doucet, A.1
Godsill, S.2
Andrieu, C.3
-
45
-
-
17744411678
-
-
E. Sudderth, A. Ihler, W. Freeman, A. Willsky, Nonparametric belief propagation, in: Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2003
-
-
-
-
46
-
-
48049094154
-
-
M. Briers, A. Doucet, S.S. Singh, K. Weekes, Particle filters for graphical models, in: Proceedings of Nonlinear Statistical Signal Processing Workshop. IEEE, 2006
-
-
-
-
47
-
-
34547481253
-
-
T. Minka, Divergence measures and message passing. Technical Report MSR-TR-2005-173, Microsoft Research, Cambridge, 2005
-
-
-
-
48
-
-
29244438430
-
Expectation consistent approximate inference
-
Opper M., and Winther O. Expectation consistent approximate inference. J. Machine Learn. Res. (2005) 2177-2204
-
(2005)
J. Machine Learn. Res.
, pp. 2177-2204
-
-
Opper, M.1
Winther, O.2
-
49
-
-
1542476948
-
-
M. Davies, N. Mitianoudis, A simple mixture model for sparse overcomplete ICA, IEE Proceedings on Vision, Image and Signal Processing, February 2004
-
-
-
|