-
1
-
-
0003278032
-
Inferring parameters and structure of latent variable models by variational Bayes
-
Morgan Kaufmann, San Francisco, CA
-
ATTIAS, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence 21-30. Morgan Kaufmann, San Francisco, CA.
-
(1999)
Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence
, pp. 21-30
-
-
Attias, H.1
-
2
-
-
84898964031
-
A variational Bayesian framework for graphical models
-
MIT Press, Cambridge, MA
-
ATTIAS, H. (2000). A variational Bayesian framework for graphical models. In Advances in Neural Information Processing Systems 12 209-215. MIT Press, Cambridge, MA.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 209-215
-
-
Attias, H.1
-
4
-
-
84878971665
-
Fast Rcpp implementation of Gauss-Hermite quadrature
-
Available at
-
BLOCKER, A. W. (2011). Fast Rcpp implementation of Gauss-Hermite quadrature. R package "fastGHQuad" version 0.1-1. Available at http://cran.r-project.org/.
-
(2011)
R package "fastGHQuad" version 0.1-1.
-
-
Blocker, A.W.1
-
5
-
-
77952563025
-
Variational inference for large-scale models of discrete choice
-
MR2757203
-
BRAUN, M. and MCAULIFFE, J. (2010). Variational inference for large-scale models of discrete choice. J. Amer. Statist. Assoc. 105 324-335. MR2757203
-
(2010)
J. Amer. Statist. Assoc.
, vol.105
, pp. 324-335
-
-
Braun, M.1
Mcauliffe, J.2
-
6
-
-
0011241944
-
Approximate inference in generalized linear mixed models
-
BRESLOW, N. E., and CLAYTON, D. G. (1993). Approximate inference in generalized linear mixed models. J. Amer. Statist. Assoc. 88 9-25.
-
(1993)
J. Amer. Statist. Assoc.
, vol.88
, pp. 9-25
-
-
Breslow, N.E.1
Clayton, D.G.2
-
7
-
-
79952088938
-
MCMC for generalized linear mixed models with glmmBUGS
-
BROWN, P., and ZHOU, L. (2010). MCMC for generalized linear mixed models with glmmBUGS. The R Journal 2 13-16.
-
(2010)
The R Journal
, vol.2
, pp. 13-16
-
-
Brown, P.1
Zhou, L.2
-
8
-
-
33847083094
-
A comparison of Bayesian, and likelihood-based methods for fitting multilevel models
-
MR2221283,(electronic)
-
BROWNE, W. J., and DRAPER, D. (2006). A comparison of Bayesian, and likelihood-based methods for fitting multilevel models. Bayesian Anal. 1 473-513 (electronic). MR2221283
-
(2006)
Bayesian Anal.
, vol.1
, pp. 473-513
-
-
Browne, W.J.1
Draper, D.2
-
9
-
-
79551691993
-
Bayesian variable selection in generalized linear mixed models
-
Springer, New York
-
CAI, B. and DUNSON, D. B. (2008). Bayesian variable selection in generalized linear mixed models. In Random Effect, and Latent Variable Model Selection. Lecture Notes in Statistics 192 63-91. Springer, New York.
-
(2008)
Random Effect, and Latent Variable Model Selection. Lecture Notes in Statistics
, vol.192
, pp. 63-91
-
-
Cai, B.1
Dunson, D.B.2
-
10
-
-
33645558077
-
Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models
-
MR2269360
-
CHRISTENSEN, O. F., ROBERTS, G. O., and SKÖLD, M. (2006). Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models. J. Comput. Graph. Statist. 15 1-17. MR2269360
-
(2006)
J. Comput. Graph. Statist.
, vol.15
, pp. 1-17
-
-
Christensen, O.F.1
Roberts, G.O.2
Sköld, M.3
-
11
-
-
4043061882
-
Variational Bayesian model selection for mixture distributions
-
Morgan Kaufmann, San Francisco, CA
-
CORDUNEANU, A., and BISHOP, C. M. (2001). Variational Bayesian model selection for mixture distributions. In Artificial Intelligence, and Statistics 27-34. Morgan Kaufmann, San Francisco, CA.
-
(2001)
Artificial Intelligence, and Statistics
, pp. 27-34
-
-
Corduneanu, A.1
Bishop, C.M.2
-
12
-
-
0031946068
-
Twelve weeks of continuous oral therapy for toenail onychomycosis caused by dermatophytes: A double-blind comparative trial of terbinafine 250 mg/day versus itraconazole 200 mg/day
-
DE BACKER, M., DE VROEY, C., LESAFFRE, E., SCHEYS, I., and DE KEYSER, P. (1998). Twelve weeks of continuous oral therapy for toenail onychomycosis caused by dermatophytes: A double-blind comparative trial of terbinafine 250 mg/day versus itraconazole 200 mg/day. Journal of the American Academy of Dermatology 38 57-63.
-
(1998)
Journal of the American Academy of Dermatology
, vol.38
, pp. 57-63
-
-
De Backer, M.1
De Vroey, C.2
Lesaffre, E.3
Scheys, I.4
De Keyser, P.5
-
13
-
-
0001708275
-
A likelihood-based method for analysing longitudinal binary responses
-
FITZMAURICE, G., and LAIRD, N. (1993). A likelihood-based method for analysing longitudinal binary responses. Biometrika 80 141-151.
-
(1993)
Biometrika
, vol.80
, pp. 141-151
-
-
Fitzmaurice, G.1
Laird, N.2
-
14
-
-
77956640383
-
Bayesian inference for generalised linear mixed models
-
FONG, Y., RUE, H., and WAKEFIELD, J. (2010). Bayesian inference for generalised linear mixed models. Biostatistics 11 397-412.
-
(2010)
Biostatistics
, vol.11
, pp. 397-412
-
-
Fong, Y.1
Rue, H.2
Wakefield, J.3
-
15
-
-
0001574731
-
Efficient parameterisations for normal linear mixed models
-
MR1366275
-
GELFAND, A. E., SAHU, S. K., and CARLIN, B. P. (1995). Efficient parameterisations for normal linear mixed models. Biometrika 82 479-488. MR1366275
-
(1995)
Biometrika
, vol.82
, pp. 479-488
-
-
Gelfand, A.E.1
Sahu, S.K.2
Carlin, B.P.3
-
16
-
-
0001516939
-
Efficient parametrizations for generalized linear mixed models
-
Oxford Univ. Press, New York. MR1425405,(Alicante, 1994)
-
GELFAND, A. E., SAHU, S. K., and CARLIN, B. P. (1996). Efficient parametrizations for generalized linear mixed models. In Bayesian Statistics 5 (Alicante, 1994) 165-180. Oxford Univ. Press, New York. MR1425405
-
(1996)
In Bayesian Statistics
, vol.5
, pp. 165-180
-
-
Gelfand, A.E.1
Sahu, S.K.2
Carlin, B.P.3
-
17
-
-
0004012196
-
-
Chapman & Hall/CRC, Boca Raton, FL. MR2027492 2nd ed
-
GELMAN, A., CARLIN, J. B., STERN, H. S., and RUBIN, D. B. (2004). Bayesian Data Analysis, 2nd ed. Chapman & Hall/CRC, Boca Raton, FL. MR2027492
-
(2004)
Bayesian Data Analysis
-
-
Gelman, A.1
Carlin, J.B.2
Stern, H.S.3
Rubin, D.B.4
-
18
-
-
84899003086
-
Propagation algorithms for variational Bayesian learning
-
MIT Press, Cambridge, MA
-
GHAHRAMANI, Z., and BEAL, M. J. (2001). Propagation algorithms for variational Bayesian learning. In Advances in Neural Information Processing Systems 13 507-513. MIT Press, Cambridge, MA.
-
(2001)
Advances in Neural Information Processing Systems
, vol.13
, pp. 507-513
-
-
Ghahramani, Z.1
Beal, M.J.2
-
19
-
-
84877740419
-
-
Available at arXiv:1206.7051
-
HOFFMAN, M. D., BLEI, D. M., WANG, C., and PAISLEY, J. (2012). Stochastic variational inference. Available at arXiv:1206.7051.
-
(2012)
Stochastic variational inference
-
-
Hoffman, M.D.1
Blei, D.M.2
Wang, C.3
Paisley, J.4
-
20
-
-
0042685161
-
Bayesian parameter estimation via variational methods
-
JAAKKOLA, T. S., and JORDAN, M. I. (2000). Bayesian parameter estimation via variational methods. Statist. Comput. 10 25-37.
-
(2000)
Statist. Comput.
, vol.10
, pp. 25-37
-
-
Jaakkola, T.S.1
Jordan, M.I.2
-
21
-
-
39849096207
-
A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne, and Draper)
-
MR2221285,(electronic)
-
KASS, R. E., and NATARAJAN, R. (2006). A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne, and Draper). Bayesian Anal. 1 535-542 (electronic). MR2221285
-
(2006)
Bayesian Anal.
, vol.1
, pp. 535-542
-
-
Kass, R.E.1
Natarajan, R.2
-
22
-
-
85162453650
-
Non-conjugate variational message passing for multinomial, and binary regression
-
Available at
-
KNOWLES, D. A., and MINKA, T. P. (2011). Non-conjugate variational message passing for multinomial, and binary regression. In Advances in Neural Information Processing Systems 24 1701-1709. Available at http://books.nips.cc/papers/files/nips24/NIPS2011_0962.pdf.
-
(2011)
Advances in Neural Information Processing Systems
, vol.24
, pp. 1701-1709
-
-
Knowles, D.A.1
Minka, T.P.2
-
23
-
-
0001303107
-
A note on Gauss-Hermite quadrature
-
MR1311107
-
LIU, Q., and PIERCE, D. A. (1994). A note on Gauss-Hermite quadrature. Biometrika 81 624-629. MR1311107
-
(1994)
Biometrika
, vol.81
, pp. 624-629
-
-
Liu, Q.1
Pierce, D.A.2
-
24
-
-
0442309501
-
Parameter expansion for data augmentation
-
MR1731488
-
LIU, J. S., and WU, Y. N. (1999). Parameter expansion for data augmentation. J. Amer. Statist. Assoc. 94 1264-1274. MR1731488
-
(1999)
J. Amer. Statist. Assoc.
, vol.94
, pp. 1264-1274
-
-
Liu, J.S.1
Wu, Y.N.2
-
25
-
-
0006407254
-
WinBUGS-A Bayesian modelling framework: Concepts, structure,, and extensibility
-
LUNN, D. J., THOMAS, A., BEST, N., and SPIEGELHALTER, D. (2000). WinBUGS-A Bayesian modelling framework: Concepts, structure,, and extensibility. Statist. Comput. 10 325-337.
-
(2000)
Statist. Comput.
, vol.10
, pp. 325-337
-
-
Lunn, D.J.1
Thomas, A.2
Best, N.3
Spiegelhalter, D.4
-
27
-
-
18244387717
-
The EM algorithm-An old folk-song sung to a fast new tune (with discussion)
-
MR1452025
-
MENG, X.-L., and VAN DYK, D. (1997). The EM algorithm-An old folk-song sung to a fast new tune (with discussion). J. R. Stat. Soc. Ser. B Stat. Methodol. 59 511-567. MR1452025
-
(1997)
J. R. Stat. Soc. Ser. B Stat. Methodol.
, vol.59
, pp. 511-567
-
-
Meng, X.-L.1
Van Dyk, D.2
-
28
-
-
0000761884
-
Seeking efficient data augmentation schemes via conditional, and marginal augmentation
-
MR1705351
-
MENG, X.-L., and VAN DYK, D. A. (1999). Seeking efficient data augmentation schemes via conditional, and marginal augmentation. Biometrika 86 301-320. MR1705351
-
(1999)
Biometrika
, vol.86
, pp. 301-320
-
-
Meng, X.-L.1
Van Dyk, D.A.2
-
29
-
-
0003772931
-
Kendall's Advanced Theory of Statistics V
-
Arnold, London 2nd ed.
-
O'HAGAN, A., and FORSTER, J. (2004). Kendall's Advanced Theory of Statistics V. 2B: Bayesian Inference, 2nd ed. Arnold, London.
-
(2004)
2B: Bayesian Inference
-
-
O'Hagan, A.1
Forster, J.2
-
30
-
-
77952563168
-
Explaining variational approximations
-
MR2757005
-
ORMEROD, J. T. and WAND, M. P. (2010). Explaining variational approximations. Amer. Statist. 64 140-153. MR2757005
-
(2010)
Amer. Statist.
, vol.64
, pp. 140-153
-
-
Ormerod, J.T.1
Wand, M.P.2
-
31
-
-
84859847512
-
Gaussian variational approximate inference for generalized linear mixed models
-
MR2913353
-
ORMEROD, J. T., and WAND, M. P. (2012). Gaussian variational approximate inference for generalized linear mixed models. J. Comput. Graph. Statist. 21 2-17. MR2913353
-
(2012)
J. Comput. Graph. Statist.
, vol.21
, pp. 2-17
-
-
Ormerod, J.T.1
Wand, M.P.2
-
32
-
-
77955397404
-
Default Bayesian model determination methods for generalised linear mixed models
-
MR2727751
-
OVERSTALL, A. M., and FORSTER, J. J. (2010). Default Bayesian model determination methods for generalised linear mixed models. Comput. Statist. Data Anal. 54 3269-3288. MR2727751
-
(2010)
Comput. Statist. Data Anal.
, vol.54
, pp. 3269-3288
-
-
Overstall, A.M.1
Forster, J.J.2
-
33
-
-
2442627902
-
Non-centered parameterizations for hierarchical models, and data augmentation
-
Oxford Univ. Press, New York. MR2003180
-
PAPASPILIOPOULOS, O., ROBERTS, G. O., and SKöLD, M. (2003). Non-centered parameterizations for hierarchical models, and data augmentation. In Bayesian Statistics 7 (Tenerife, 2002) 307-326. Oxford Univ. Press, New York. MR2003180
-
(2003)
Bayesian Statistics 7 (Tenerife, 2002)
, pp. 307-326
-
-
Papaspiliopoulos, O.1
Roberts, G.O.2
Sköld, M.3
-
34
-
-
34249101736
-
A general framework for the parametrization of hierarchical models
-
MR2408661
-
PAPASPILIOPOULOS, O., ROBERTS, G. O., and SKöLD, M. (2007). A general framework for the parametrization of hierarchical models. Statist. Sci. 22 59-73. MR2408661
-
(2007)
Statist. Sci.
, vol.22
, pp. 59-73
-
-
Papaspiliopoulos, O.1
Roberts, G.O.2
Sköld, M.3
-
35
-
-
84864056575
-
Parameter expanded variational Bayesian methods
-
MIT Press, Cambridge, MA
-
QI, Y., and JAAKKOLA, T. S. (2006). Parameter expanded variational Bayesian methods. In Advances in Neural Information Processing Systems 19 1097-1104. MIT Press, Cambridge, MA.
-
(2006)
Advances in Neural Information Processing Systems
, vol.19
, pp. 1097-1104
-
-
Qi, Y.1
Jaakkola, T.S.2
-
36
-
-
77950035264
-
Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation
-
MR1826278
-
RAUDENBUSH, S. W., YANG, M.-L., and YOSEF, M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. J. Comput. Graph. Statist. 9 141-157. MR1826278
-
(2000)
J. Comput. Graph. Statist.
, vol.9
, pp. 141-157
-
-
Raudenbush, S.W.1
Yang, M.-L.2
Yosef, M.3
-
37
-
-
49749118319
-
Assessing the performance of variational methods for mixed logistic regression models
-
MR2528235
-
RIJMEN, F., and VOMLEL, J. (2008). Assessing the performance of variational methods for mixed logistic regression models. J. Stat. Comput. Simul. 78 765-779. MR2528235
-
(2008)
J. Stat. Comput. Simul.
, vol.78
, pp. 765-779
-
-
Rijmen, F.1
Vomlel, J.2
-
38
-
-
79958717589
-
Sensitivity analysis in Bayesian generalized linear mixed models for binary data
-
MR2806244
-
ROOS, M., and HELD, L. (2011). Sensitivity analysis in Bayesian generalized linear mixed models for binary data. Bayesian Anal. 6 259-278. MR2806244
-
(2011)
Bayesian Anal.
, vol.6
, pp. 259-278
-
-
Roos, M.1
Held, L.2
-
39
-
-
35148885089
-
Nestling barn owls beg more intensely in the presence of their mother than in the presence of their father
-
ROULIN, A., and BERSIER, L. F. (2007). Nestling barn owls beg more intensely in the presence of their mother than in the presence of their father. Animal Behaviour 74 1099-1106.
-
(2007)
Animal Behaviour
, vol.74
, pp. 1099-1106
-
-
Roulin, A.1
Bersier, L.F.2
-
40
-
-
0003383444
-
A mean field learning algorithm for unsupervised neural networks
-
Kluwer Academic, Dordrecht
-
SAUL, L. K., and JORDAN (1998). A mean field learning algorithm for unsupervised neural networks. In Learning in Graphical Models 541-554. Kluwer Academic, Dordrecht.
-
(1998)
Learning in Graphical Models
, pp. 541-554
-
-
Saul, L.K.1
Jordan2
-
42
-
-
84878983726
-
Variational approximation for mixtures of linear mixed models
-
To appear. DOI:10.1080/10618600.2012.761138
-
TAN, S. L., and NOTT, D. J. (2013). Variational approximation for mixtures of linear mixed models. J. Comput. Graph. Statist. To appear. DOI:10.1080/10618600.2012.761138.
-
(2013)
J. Comput. Graph. Statist.
-
-
Tan, S.L.1
Nott, D.J.2
-
43
-
-
0025172155
-
Some covariance models for longitudinal count data with overdispersion
-
MR1085814
-
THALL, P. F., and VAIL, S. C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46 657-671. MR1085814
-
(1990)
Biometrics
, vol.46
, pp. 657-671
-
-
Thall, P.F.1
Vail, S.C.2
-
46
-
-
21844450606
-
Variational message passing
-
MR2249835
-
WINN, J., and BISHOP, C. M. (2005). Variational message passing. J. Mach. Learn. Res. 6 661-694. MR2249835
-
(2005)
J. Mach. Learn. Res.
, vol.6
, pp. 661-694
-
-
Winn, J.1
Bishop, C.M.2
-
47
-
-
80053346023
-
To center or not to center: That is not the question-An ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC efficiency
-
MR2878987
-
YU, Y. and MENG, X.-L. (2011). To center or not to center: That is not the question-An ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC efficiency. J. Comput. Graph. Statist. 20 531-570. MR2878987
-
(2011)
J. Comput. Graph. Statist.
, vol.20
, pp. 531-570
-
-
Yu, Y.1
Meng, X.-L.2
-
48
-
-
80455176896
-
Conditional Akaike information criterion for generalized linear mixed models
-
MR2853760
-
YU, D., and YAU, K. K. W. (2012). Conditional Akaike information criterion for generalized linear mixed models. Comput. Statist. Data Anal. 56 629-644. MR2853760
-
(2012)
Comput. Statist. Data Anal.
, vol.56
, pp. 629-644
-
-
Yu, D.1
Yau, K.K.W.2
-
49
-
-
33745929657
-
General design Bayesian generalized linear mixed models
-
MR2275966
-
ZHAO, Y., STAUDENMAYER, J., COULL, B. A. and WAND, M. P. (2006). General design Bayesian generalized linear mixed models. Statist. Sci. 21 35-51. MR2275966
-
(2006)
Statist. Sci.
, vol.21
, pp. 35-51
-
-
Zhao, Y.1
Staudenmayer, J.2
Coull, B.A.3
Wand, M.P.4
-
50
-
-
59349116454
-
-
Springer, New York. MR2722501
-
ZUUR, A. F., IENO, E. N.,WALKER, N. J., SAVELIEV, A. A., and SMITH, G. M. (2009). Mixed Effects Models, and Extensions in Ecology with R. Springer, New York. MR2722501
-
(2009)
Mixed Effects Models, and Extensions in Ecology with R
-
-
Zuur, A.F.1
Ieno, E.N.2
Walker, N.J.3
Saveliev, A.A.4
Smith, G.M.5
|