-
2
-
-
0027560587
-
Approximating probabilistic inference in Bayesian belief networks is NP-hard
-
DOI 10.1016/0004-3702(93)90036-B
-
P. Dagum and M. Luby. Approximating probabilistic inference in bayesian belief networks is NP-hard. Artificial Intelligence, 60(1):141-153, 1993. (Pubitemid 23622908)
-
(1993)
Artificial Intelligence
, vol.60
, Issue.1
, pp. 141-153
-
-
Dagum Paul1
Luby Michael2
-
4
-
-
0000388721
-
Generalized belief propagation
-
Jonathan S. Yedidia,William T. Freeman, and YairWeiss. Generalized belief propagation. In NIPS, pages 689-695, 2000.
-
(2000)
NIPS
, pp. 689-695
-
-
Yedidia, J.S.1
Freeman, W.T.2
Weiss, Y.3
-
5
-
-
0345978970
-
Expectation propagation for approximate bayesian inference
-
T. Minka. Expectation propagation for approximate bayesian inference. In Proc. UAI, pages 362-369, 2001.
-
(2001)
Proc. UAI
, pp. 362-369
-
-
Minka, T.1
-
6
-
-
0001361993
-
The two-filter formula for smoothing and an implementation of the gaussian-sum smoother
-
G. Kitagawa. The two-filter formula for smoothing and an implementation of the gaussian-sum smoother. Ann. Inst. Statist. Math., 46(4):605-623, 1994.
-
(1994)
Ann. Inst. Statist. Math.
, vol.46
, Issue.4
, pp. 605-623
-
-
Kitagawa, G.1
-
9
-
-
17744411678
-
Nonparametric belief propagation
-
E. Sudderth, A. Ihler, W. Freeman, and A. Willsky. Nonparametric belief propagation. In Proc. CVPR, volume 1, pages 605-612, 2003.
-
(2003)
Proc. CVPR
, vol.1
, pp. 605-612
-
-
Sudderth, E.1
Ihler, A.2
Freeman, W.3
Willsky, A.4
-
10
-
-
17544404795
-
Pampas: Real-valued graphical models for computer vision
-
M. Isard. Pampas: Real-valued graphical models for computer vision. In Proc. CVPR, volume 1, pages 613-620, 2003.
-
(2003)
Proc. CVPR
, vol.1
, pp. 613-620
-
-
Isard, M.1
-
11
-
-
0035246564
-
Factor graphs and the sum-product algorithm
-
DOI 10.1109/18.910572, PII S0018944801007210
-
F.R. Kschischang, B.J. Frey, and H.A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2):498-519, 2001. (Pubitemid 32318087)
-
(2001)
IEEE Transactions on Information Theory
, vol.47
, Issue.2
, pp. 498-519
-
-
Kschischang, F.R.1
Frey, B.J.2
Loeliger, H.-A.3
-
12
-
-
33745618417
-
Deterministic approximate inference techniques for conditionally Gaussian state space models
-
DOI 10.1007/s11222-006-8770-8
-
O. Zoeter and H. Heskes. Deterministic approximate inference techniques for conditionally gaussian state space models. Statistics and Computing, 16(3):279-292, 2006. (Pubitemid 43992696)
-
(2006)
Statistics and Computing
, vol.16
, Issue.3
, pp. 279-292
-
-
Zoeter, O.1
Heskes, T.2
-
14
-
-
0006416672
-
Nonuniform dynamic discretization in hybrid networks
-
Alexander V. Kozlov and Daphne Koller. Nonuniform dynamic discretization in hybrid networks. In Proc. UAI, pages 314-325, 1997.
-
(1997)
Proc. UAI
, pp. 314-325
-
-
Kozlov, A.V.1
Koller, D.2
-
15
-
-
0016557674
-
Multidimensional binary search trees used for associative searching
-
Jon Louis Bentley. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9):509-517, 1975.
-
(1975)
Commun. ACM
, vol.18
, Issue.9
, pp. 509-517
-
-
Louis Bentley, J.1
-
17
-
-
84949966985
-
Finding deformable shapes using loopy belief propagation
-
J. Coughlan and S. Ferreira. Finding deformable shapes using loopy belief propagation. In Proc. ECCV, pages 453-468, 2002.
-
(2002)
Proc. ECCV
, pp. 453-468
-
-
Coughlan, J.1
Ferreira, S.2
-
18
-
-
84932651113
-
Shape matching with belief propagation: Using dynamic quantization to accommodate occlusion and clutter
-
J. Coughlan and H. Shen. Shape matching with belief propagation: Using dynamic quantization to accommodate occlusion and clutter. In Proc. Workshop on Generative-Model Based Vision, 2004.
-
(2004)
Proc. Workshop on Generative-Model Based Vision
-
-
Coughlan, J.1
Shen, H.2
-
19
-
-
33947697927
-
Sparse forward-backward using minimum divergence beams for fast training of conditional random fields
-
C. Pal, C. Sutton, and A. McCallum. Sparse forward-backward using minimum divergence beams for fast training of conditional random fields. In International Conference on Acoustics, Speech, and Signal Processing, 2006.
-
(2006)
International Conference on Acoustics, Speech, and Signal Processing
-
-
Pal, C.1
Sutton, C.2
McCallum, A.3
|