-
3
-
-
84898985963
-
Approximating posterior distributions in belief networks using mixtures
-
Bishop, C.M., Lawrence, N., Jaakkola, T., and Jordan, M.I. Approximating posterior distributions in belief networks using mixtures. NIPS, 1998.
-
(1998)
NIPS
-
-
Bishop, C.M.1
Lawrence, N.2
Jaakkola, T.3
Jordan, M.I.4
-
4
-
-
71149088292
-
Split variational inference
-
ACM
-
Bouchard, G. and Zoeter, O. Split variational inference. In ICML, pp. 57-64. ACM, 2009.
-
(2009)
ICML
, pp. 57-64
-
-
Bouchard, G.1
Zoeter, O.2
-
5
-
-
77952563025
-
Variational inference for largescale models of discrete choice
-
Braun, M. and McAuliffe, J. Variational inference for largescale models of discrete choice. Journal of the American Statistical Association, 105(489):324-335, 2010.
-
(2010)
Journal of the American Statistical Association
, vol.105
, Issue.489
, pp. 324-335
-
-
Braun, M.1
McAuliffe, J.2
-
6
-
-
79956217022
-
A topographic latent source model for fMRI data
-
Gershman, S.J., Blei, D.M., Pereira, F., and Norman, K.A. A topographic latent source model for fMRI data. NeuroImage, 57:89-100, 2011.
-
(2011)
NeuroImage
, vol.57
, pp. 89-100
-
-
Gershman, S.J.1
Blei, D.M.2
Pereira, F.3
Norman, K.A.4
-
7
-
-
34547474118
-
Blind separation of nonlinear mixtures by variational bayesian learning
-
Honkela, A., Valpola, H., Ilin, A., and Karhunen, J. Blind separation of nonlinear mixtures by variational bayesian learning. Digital Signal Processing, 17(5):914-934, 2007.
-
(2007)
Digital Signal Processing
, vol.17
, Issue.5
, pp. 914-934
-
-
Honkela, A.1
Valpola, H.2
Ilin, A.3
Karhunen, J.4
-
8
-
-
67650524768
-
On entropy approximation for gaussian mixture random vectors
-
IEEE
-
Huber, M.F., Bailey, T., Durrant-Whyte, H., and Hanebeck, U.D. On entropy approximation for gaussian mixture random vectors, In Multisensor Fusion and Integration for Intelligent Systems, pp. 181-188. IEEE, 2008.
-
(2008)
Multisensor Fusion and Integration for Intelligent Systems
, pp. 181-188
-
-
Huber, M.F.1
Bailey, T.2
Durrant-Whyte, H.3
Hanebeck, U.D.4
-
9
-
-
77957961174
-
Particle-based variational inference for continuous systems
-
Ihler, A.T., Frank, A.J., and Smyth, P. Particle-based variational inference for continuous systems. NIPS, 2009.
-
(2009)
NIPS
-
-
Ihler, A.T.1
Frank, A.J.2
Smyth, P.3
-
10
-
-
0042685161
-
Bayesian parameter estimation via variational methods
-
Jaakkola, T.S. and Jordan, M.I. Bayesian parameter estimation via variational methods. Statistics and Computing, 10(1):25-37, 2000.
-
(2000)
Statistics and Computing
, vol.10
, Issue.1
, pp. 25-37
-
-
Jaakkola, T.S.1
Jordan, M.I.2
-
11
-
-
0001837853
-
Improving the mean field approximation via the use of mixture distributions
-
Jaakola, T.S. and Jordan, M.I. Improving the mean field approximation via the use of mixture distributions. In Learning Graphical Models. 1998.
-
(1998)
Learning Graphical Models
-
-
Jaakola, T.S.1
Jordan, M.I.2
-
12
-
-
0033225865
-
An introduction to variational methods for graphical models
-
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
-
(1999)
Machine Learning
, vol.37
, Issue.2
, pp. 183-233
-
-
Jordan, M.I.1
Ghahramani, Z.2
Jaakkola, T.S.3
Saul, L.K.4
-
13
-
-
85162389868
-
Variational bounds for mixed-data factor analysis
-
Khan, M.E., Marlin, B., Bouchard, G., and Murphy, K. Variational bounds for mixed-data factor analysis. In NIPS 23. 2010.
-
(2010)
NIPS
, vol.23
-
-
Khan, M.E.1
Marlin, B.2
Bouchard, G.3
Murphy, K.4
-
15
-
-
1542714899
-
Reducing the variability in cDNA microarray image processing by Bayesian inference
-
Lawrence, N.D., Milo, M., Niranjan, M., Rashbass, P., and Soullier, S. Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics, 20(4):518-526, 2004.
-
(2004)
Bioinformatics
, vol.20
, Issue.4
, pp. 518-526
-
-
Lawrence, N.D.1
Milo, M.2
Niranjan, M.3
Rashbass, P.4
Soullier, S.5
-
16
-
-
0001441372
-
Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks
-
MacKay, D.J.C. Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 6(3):469-505, 1995.
-
(1995)
Network: Computation in Neural Systems
, vol.6
, Issue.3
, pp. 469-505
-
-
MacKay, D.J.C.1
-
17
-
-
0033337021
-
Fisher discriminant analysis with kernels
-
IEEE
-
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, KR. Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX, pp. 41-48. IEEE, 1999.
-
(1999)
Neural Networks for Signal Processing IX
, pp. 41-48
-
-
Mika, S.1
Ratsch, G.2
Weston, J.3
Scholkopf, B.4
Mullers, K.R.5
-
18
-
-
85057196821
-
MCMC using Hamiltonian dynamics
-
Brooks, S., German, A., Jones, G., and Meng, X.-L. (eds.), Chapman & Hall / CRC Press
-
Neal, R.M. MCMC using Hamiltonian dynamics. In Brooks, S., German, A., Jones, G., and Meng, X.-L. (eds.), Handbook of Markov Chain Monte Carlo. Chapman & Hall / CRC Press, 2011.
-
(2011)
Handbook of Markov Chain Monte Carlo
-
-
Neal, R.M.1
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