-
2
-
-
0000708831
-
Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
-
Antoniak CE. 1974. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Stat. 2:1152-74
-
(1974)
Ann. Stat
, vol.2
, pp. 1152-1174
-
-
Antoniak, C.E.1
-
4
-
-
0003278032
-
Inferring parameters and structure of latent variable models by variational Bayes
-
Attias H. 1999. Inferring parameters and structure of latent variable models by variational Bayes. Proc. Conf. Uncertain. Artif. Intell. 15:21-30
-
(1999)
Proc. Conf. Uncertain. Artif. Intell
, vol.15
, pp. 21-30
-
-
Attias, H.1
-
5
-
-
84898964031
-
A variational Bayesian framework for graphical models
-
Attias H. 2000. A variational Bayesian framework for graphical models. Adv. Neural Inf. Process. Syst. 12:209-15
-
(2000)
Adv. Neural Inf. Process. Syst
, vol.12
, pp. 209-215
-
-
Attias, H.1
-
9
-
-
0029043932
-
The analysis of repeated-measures data on schizophrenic reaction times using mixture models
-
Belin TR, Rubin DB. 1995. The analysis of repeated-measures data on schizophrenic reaction times using mixture models. Stat. Med. 14:747-68
-
(1995)
Stat. Med
, vol.14
, pp. 747-768
-
-
Belin, T.R.1
Rubin, D.B.2
-
10
-
-
57349146373
-
Lessons from the Netflix prize challenge
-
Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75-79
-
(2007)
ACM SIGKDD Explor. Newsl
, vol.9
, pp. 75-79
-
-
Bell, R.M.1
Koren, Y.2
-
13
-
-
84874128594
-
Model-based machine learning
-
Bishop CM. 2013. Model-based machine learning. Philos. Trans. R. Soc. A 371:20120222
-
(2013)
Philos. Trans. R. Soc. A
, vol.371
, pp. 20120222
-
-
Bishop, C.M.1
-
15
-
-
84861170800
-
Probabilistic topic models
-
Blei DM. 2012. Probabilistic topic models. Commun. ACM 55:77-84
-
(2012)
Commun ACM
, vol.55
, pp. 77-84
-
-
Blei, D.M.1
-
17
-
-
52449116403
-
A correlated topic model of science
-
Blei DM, Lafferty JD. 2007. A correlated topic model of science. Ann. Appl. Stat. 1:17-35
-
(2007)
Ann. Appl. Stat
, vol.1
, pp. 17-35
-
-
Blei, D.M.1
Lafferty, J.D.2
-
19
-
-
0000025871
-
Science and statistics
-
Box GEP. 1976. Science and statistics. J. Am. Stat. Assoc. 71:791-99
-
(1976)
J. Am. Stat. Assoc
, vol.71
, pp. 791-799
-
-
Box, G.E.P.1
-
20
-
-
0001135785
-
Sampling and Bayes' inference in scientific modelling and robustness
-
Box GEP. 1980. Sampling and Bayes' inference in scientific modelling and robustness. J. R. Stat. Soc. A 143:383-430
-
(1980)
J. R. Stat. Soc. A
, vol.143
, pp. 383-430
-
-
Box, G.E.P.1
-
22
-
-
0002928751
-
Discrimination among mechanistic models
-
Box GEP, Hill WJ. 1967. Discrimination among mechanistic models. Technometrics 9:57-71
-
(1967)
Technometrics
, vol.9
, pp. 57-71
-
-
Box, G.E.P.1
Hill, W.J.2
-
23
-
-
3042895415
-
A useful method for model-building
-
Box GEP, Hunter WG. 1962. A useful method for model-building. Technometrics 4:301-18
-
(1962)
Technometrics
, vol.4
, pp. 301-318
-
-
Box, G.E.P.1
Hunter, W.G.2
-
24
-
-
0011302720
-
The experimental study of physical mechanisms
-
Box GEP, Hunter WG. 1965. The experimental study of physical mechanisms. Technometrics 7:23-42
-
(1965)
Technometrics
, vol.7
, pp. 23-42
-
-
Box, G.E.P.1
Hunter, W.G.2
-
29
-
-
84899014910
-
A generalization of principal component analysis to the exponential family
-
Collins M, Dasgupta S, Schapire R. 2002. A generalization of principal component analysis to the exponential family. Adv. Neural Inf. Process. Syst. 14:617-24
-
(2002)
Adv. Neural Inf. Process. Syst
, vol.14
, pp. 617-624
-
-
Collins, M.1
Dasgupta, S.2
Schapire, R.3
-
31
-
-
21344482755
-
Hyper Markov laws in the statistical analysis of decomposable graphical models
-
Dawid AP, Lauritzen SL. 1993. Hyper Markov laws in the statistical analysis of decomposable graphical models. Ann. Stat. 21:1272-317
-
(1993)
Ann. Stat
, vol.21
, pp. 1272-1317
-
-
Dawid, A.P.1
Lauritzen, S.L.2
-
32
-
-
0002629270
-
Maximum likelihood from incomplete data via the em algorithm
-
Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B. 36:1-38
-
(1977)
J. R. Stat. Soc. B
, vol.36
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
33
-
-
0002006630
-
Theories of data analysis: From magical thinking through classical statistics
-
New York Wiley, ed. DC Hoaglin, F Mosteller, JW Tukey
-
Diaconis P. 1985. Theories of data analysis: from magical thinking through classical statistics. In Exploring Data: Tables, Trends, and Shapes, ed. DC Hoaglin, F Mosteller, JW Tukey, pp. 1-36. New York: Wiley
-
(1985)
Exploring Data: Tables, Trends, and Shapes
, pp. 1-36
-
-
Diaconis, P.1
-
35
-
-
84906870368
-
Empirical Bayes modeling, computation, and accuracy
-
Div. Biostat., Stanford Univ., Stanford, CA
-
Efron B. 2013. Empirical Bayes modeling, computation, and accuracy. Tech. rep. 263, Div. Biostat., Stanford Univ., Stanford, CA. http://statweb.stanford. edu/~ckirby/brad/papers/2013EBModeling.pdf
-
(2013)
Tech. Rep
, vol.263
-
-
Efron, B.1
-
36
-
-
0000053186
-
Combining possibly related estimation problems
-
Efron B, Morris C. 1973. Combining possibly related estimation problems. J. R. Stat. Soc. B 35:379-421
-
(1973)
J. R. Stat. Soc. B
, vol.35
, pp. 379-421
-
-
Efron, B.1
Morris, C.2
-
38
-
-
52949126188
-
Describing disability through individual-level mixture models for multivariate binary data
-
Erosheva EA, Fienberg SE, Joutard C. 2007. Describing disability through individual-level mixture models for multivariate binary data. Ann. Appl. Stat. 1:502-37
-
(2007)
Ann. Appl. Stat
, vol.1
, pp. 502-537
-
-
Erosheva, E.A.1
Fienberg, S.E.2
Joutard, C.3
-
39
-
-
0001120413
-
A Bayesian analysis of some nonparametric problems
-
Ferguson TS. 1973. A Bayesian analysis of some nonparametric problems. Ann. Stat. 1:209-30
-
(1973)
Ann. Stat
, vol.1
, pp. 209-230
-
-
Ferguson, T.S.1
-
40
-
-
84950645271
-
The predictive sample reuse method with applications
-
Geisser S. 1975. The predictive sample reuse method with applications. J. Am. Stat. Assoc. 70:320-28
-
(1975)
J. Am. Stat. Assoc
, vol.70
, pp. 320-328
-
-
Geisser, S.1
-
41
-
-
84950453304
-
Sampling-based approaches to calculating marginal densities
-
Gelfand AE, Smith AFM. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85:398-409
-
(1990)
J. Am. Stat. Assoc
, vol.85
, pp. 398-409
-
-
Gelfand, A.E.1
Smith, A.F.M.2
-
44
-
-
25444484077
-
Posterior predictive assessment of model fitness via realized discrepancies
-
Gelman A, Meng X-L, Stern H. 1996. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6:733-807
-
(1996)
Stat. Sin
, vol.6
, pp. 733-807
-
-
Gelman, A.1
Meng, X.-L.2
Stern, H.3
-
45
-
-
84872536689
-
Philosophy and the practice of Bayesian statistics
-
Gelman A, Shalizi CR. 2012. Philosophy and the practice of Bayesian statistics. Br. J. Math. Stat. Psychol. 66:8-38
-
(2012)
Br. J. Math. Stat. Psychol
, vol.66
, pp. 8-38
-
-
Gelman, A.1
Shalizi, C.R.2
-
46
-
-
15044358532
-
Multiple imputation for model checking: Completed-data plots with missing and latent data
-
Gelman A, Van Mechelen I, Verbeke G, Heitjan DF, Meulders M. 2005. Multiple imputation for model checking: completed-data plots with missing and latent data. Biometrics 61:74-85
-
(2005)
Biometrics
, vol.61
, pp. 74-85
-
-
Gelman, A.1
Van Mechelen, I.2
Verbeke, G.3
Heitjan, D.F.4
Meulders, M.5
-
47
-
-
0021518209
-
Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images
-
Geman S, Geman D. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:721-41
-
(1984)
IEEE Trans. Pattern Anal. Mach. Intell
, vol.6
, pp. 721-741
-
-
Geman, S.1
Geman, D.2
-
48
-
-
84857235576
-
A tutorial on Bayesian nonparametric models
-
Gershman SJ, Blei DM. 2012. A tutorial on Bayesian nonparametric models. J. Math. Psychol. 56:1-12
-
(2012)
J. Math. Psychol
, vol.56
, pp. 1-12
-
-
Gershman, S.J.1
Blei, D.M.2
-
49
-
-
84874126433
-
Bayesian nonparametrics and the probabilistic approach to modelling
-
Ghahramani Z. 2012. Bayesian nonparametrics and the probabilistic approach to modelling. Philos. Trans. R. Soc. A 371:1984
-
(2012)
Philos. Trans. R. Soc. A
, vol.371
, pp. 1984
-
-
Ghahramani, Z.1
-
50
-
-
84899003086
-
Propagation algorithms for variational Bayesian learning
-
Ghahramani Z, Beal MJ. 2001. Propagation algorithms for variational Bayesian learning. Adv. Neural Inf. Process. Syst. 13:507-13
-
(2001)
Adv. Neural Inf. Process. Syst
, vol.13
, pp. 507-513
-
-
Ghahramani, Z.1
Beal, M.J.2
-
51
-
-
0001178202
-
A language and program for complex Bayesian modelling
-
Gilks WR, Thomas A, Spiegelhalter DJ. 1992. A language and program for complex Bayesian modelling. Statistician 43:169-77
-
(1992)
Statistician
, vol.43
, pp. 169-177
-
-
Gilks, W.R.1
Thomas, A.2
Spiegelhalter, D.J.3
-
52
-
-
84926272958
-
The philosophy of exploratory data analysis
-
Good IJ. 1983. The philosophy of exploratory data analysis. Philos. Sci. 50:283-95
-
(1983)
Philos. Sci
, vol.50
, pp. 283-295
-
-
Good, I.J.1
-
54
-
-
0002876195
-
The use of the concept of a future observation in goodness-of-fit problems
-
Guttman I. 1967. The use of the concept of a future observation in goodness-of-fit problems. J. R. Stat. Soc. B 29:83-100
-
(1967)
J. R. Stat. Soc. B
, vol.29
, pp. 83-100
-
-
Guttman, I.1
-
55
-
-
77956890234
-
Monte Carlo sampling methods using Markov chains and their applications
-
Hastings WK. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97-109
-
(1970)
Biometrika
, vol.57
, pp. 97-109
-
-
Hastings, W.K.1
-
58
-
-
58149421595
-
Analysis of a complex of statistical variables into principal components
-
Hotelling H. 1933. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24:417-41
-
(1933)
J. Educ. Psychol
, vol.24
, pp. 417-441
-
-
Hotelling, H.1
-
59
-
-
0000107975
-
Relations between two sets of variates
-
Hotelling H. 1936. Relations between two sets of variates. Biometrika 28:321-77
-
(1936)
Biometrika
, vol.28
, pp. 321-377
-
-
Hotelling, H.1
-
60
-
-
22944460748
-
Spike and slab variable selection: Frequentist and Bayesian strategies
-
Ishwaran H, Rao JS. 2005. Spike and slab variable selection: frequentist and Bayesian strategies. Ann. Stat. 33:730-73
-
(2005)
Ann. Stat
, vol.33
, pp. 730-773
-
-
Ishwaran, H.1
Rao, J.S.2
-
62
-
-
4043129651
-
Graphical models
-
Jordan MI. 2004. Graphical models. Stat. Sci. 19:140-55
-
(2004)
Stat. Sci
, vol.19
, pp. 140-155
-
-
Jordan, M.I.1
-
64
-
-
85024429815
-
A new approach to linear filtering and prediction problems
-
Kalman RE. 1960. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82:35-45
-
(1960)
Trans. ASME J. Basic Eng
, vol.82
, pp. 35-45
-
-
Kalman, R.E.1
-
66
-
-
85162453650
-
Non-conjugate variational message passing for multinomial and binary regression
-
Knowles DA, Minka TP. 2011. Non-conjugate variational message passing for multinomial and binary regression. Adv. Neural Inf. Process. Syst. 24:1701-9
-
(2011)
Adv. Neural Inf. Process. Syst
, vol.24
, pp. 1701-1709
-
-
Knowles, D.A.1
Minka, T.P.2
-
68
-
-
85008044987
-
Matrix factorization techniques for recommender systems
-
Koren Y, Bell R, Volinsky C. 2009. Matrix factorization techniques for recommender systems. Computer 42:30-37
-
(2009)
Computer
, vol.42
, pp. 30-37
-
-
Koren, Y.1
Bell, R.2
Volinsky, C.3
-
69
-
-
35948952011
-
Parametric and nonparametric Bayesian model specification: A case study involving models for count data
-
Krnjajić M, Kottas A, Draper D. 2008. Parametric and nonparametric Bayesian model specification: a case study involving models for count data. Comput. Stat. Data Anal. 52:2110-28
-
(2008)
Comput. Stat. Data Anal
, vol.52
, pp. 2110-2128
-
-
Krnjajić, M.1
Kottas, A.2
Draper, D.3
-
71
-
-
84906856853
-
Discussion of some aspects of model selection for prediction: Article of Chakrabarti and Ghosh
-
ed. JM Bernardo, MJ Bayarri, JO Berger, AP David, D Heckerman, et al., Oxford, UK: Oxford Univ. Press
-
Lauritzen SL. 2007. Discussion of some aspects of model selection for prediction: article of Chakrabarti and Ghosh. In Bayesian Statistics 8, ed. JM Bernardo, MJ Bayarri, JO Berger, AP David, D Heckerman, et al., pp. 84-90. Oxford, UK: Oxford Univ. Press
-
(2007)
Bayesian Statistics
, vol.8
, pp. 84-90
-
-
Lauritzen, S.L.1
-
72
-
-
0033592606
-
Learning the parts of objects by non-negative matrix factorization
-
Lee DD, Seung HS. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401:788-91
-
(1999)
Nature
, vol.401
, pp. 788-791
-
-
Lee, D.D.1
Seung, H.S.2
-
73
-
-
84972487778
-
Model specification: The views of Fisher and Neyman, and later developments
-
Lehmann EL. 1990. Model specification: the views of Fisher and Neyman, and later developments. Stat. Sci. 5(2):160-68
-
(1990)
Stat. Sci
, vol.5
, Issue.2
, pp. 160-168
-
-
Lehmann, E.L.1
-
75
-
-
4444311171
-
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny
-
McAuliffe JD, Pachter L, Jordan MI. 2004. Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinformatics 20:1850-60
-
(2004)
Bioinformatics
, vol.20
, pp. 1850-1860
-
-
McAuliffe, J.D.1
Pachter, L.2
Jordan, M.I.3
-
76
-
-
84863338363
-
FACTORIE: Probabilistic programming via imperatively defined factor graphs
-
McCallum A, Schultz K, Singh S. 2009. FACTORIE: probabilistic programming via imperatively defined factor graphs. Adv. Neural Inf. Process. Syst. 22:1249-57
-
(2009)
Adv. Neural Inf. Process. Syst
, vol.22
, pp. 1249-1257
-
-
McCallum, A.1
Schultz, K.2
Singh, S.3
-
78
-
-
0042742904
-
Posterior predictive p-values
-
Meng X-L. 1994. Posterior predictive p-values. Ann. Stat. 22:1142-60
-
(1994)
Ann. Stat
, vol.22
, pp. 1142-1160
-
-
Meng, X.-L.1
-
79
-
-
5744249209
-
Equations of state calculations by fast computing machines
-
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. 1953. Equations of state calculations by fast computing machines. J. Chem. Phys. 21:1087-92
-
(1953)
J. Chem. Phys
, vol.21
, pp. 1087-1092
-
-
Metropolis, N.1
Rosenbluth, A.W.2
Rosenbluth, M.N.3
Teller, A.H.4
Teller, E.5
-
82
-
-
84950432453
-
Parametric empirical Bayes inference: Theory and applications
-
Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47-65
-
(1983)
J. Am. Stat. Assoc
, vol.78
, pp. 47-65
-
-
Morris, C.N.1
-
84
-
-
0002788893
-
A view of the em algorithm that justifies incremental, sparse, and other variants
-
Cambridge, MA MIT Press, ed. MI Jordan
-
Neal RM, Hinton GE. 1999. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, ed. MI Jordan, pp. 355-68. Cambridge, MA: MIT Press
-
(1999)
Learning in Graphical Models
, pp. 355-368
-
-
Neal, R.M.1
Hinton, G.E.2
-
87
-
-
0000325341
-
On lines and planes of closest fit to systems of points
-
Pearson K. 1901. On lines and planes of closest fit to systems of points. Lond. Edinb. Dublin Philos. Mag. J. Sci. 6:559-72
-
(1901)
Lond. Edinb. Dublin Philos. Mag. J. Sci
, vol.6
, pp. 559-572
-
-
Pearson, K.1
-
88
-
-
0001406440
-
A mean field theory learning algorithm for neural networks
-
Peterson C, Anderson JR. 1987. A mean field theory learning algorithm for neural networks. Complex Syst. 1:995-1019
-
(1987)
Complex Syst
, vol.1
, pp. 995-1019
-
-
Peterson, C.1
Anderson, J.R.2
-
90
-
-
0034118493
-
Inference of population structure using multilocus genotype data
-
Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-59
-
(2000)
Genetics
, vol.155
, pp. 945-959
-
-
Pritchard, J.K.1
Stephens, M.2
Donnelly, P.3
-
91
-
-
0024610919
-
A tutorial on hidden Markov models and selected applications in speech recognition
-
Rabiner LR. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77:257-86
-
(1989)
Proc IEEE
, vol.77
, pp. 257-286
-
-
Rabiner, L.R.1
-
92
-
-
0347124352
-
An empirical Bayes estimation problem
-
Robbins H. 1980. An empirical Bayes estimation problem. Proc. Natl. Acad. Sci. USA 77:6988-89
-
(1980)
Proc. Natl. Acad. Sci. USA
, vol.77
, pp. 6988-6989
-
-
Robbins, H.1
-
93
-
-
0000016172
-
A stochastic approximation method
-
Robbins H, Monro S. 1951. A stochastic approximation method. Ann. Math. Stat. 22:400-7
-
(1951)
Ann. Math. Stat
, vol.22
, pp. 400-407
-
-
Robbins, H.1
Monro, S.2
-
95
-
-
84898929664
-
EM algorithms for PCA and SPCA
-
Roweis S. 1998. EM algorithms for PCA and SPCA. Adv. Neural Inf. Process. Syst. 10:626-32
-
(1998)
Adv. Neural Inf. Process. Syst
, vol.10
, pp. 626-632
-
-
Roweis, S.1
-
96
-
-
0000439370
-
Bayesianly justifiable and relevant frequency calculations for the applied statistician
-
Rubin DB. 1984. Bayesianly justifiable and relevant frequency calculations for the applied statistician. Ann. Stat. 12:1151-72
-
(1984)
Ann. Stat
, vol.12
, pp. 1151-1172
-
-
Rubin, D.B.1
-
97
-
-
62849120031
-
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
-
Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71:319-92
-
(2009)
J. R. Stat. Soc. B
, vol.71
, pp. 319-392
-
-
Rue, H.1
Martino, S.2
Chopin, N.3
-
99
-
-
85156241149
-
Exploiting tractable substructures in intractable networks
-
Saul LK, Jordan MI. 1996. Exploiting tractable substructures in intractable networks. Adv. Neural Inf. Process. Syst. 8:486-92
-
(1996)
Adv. Neural Inf. Process. Syst
, vol.8
, pp. 486-492
-
-
Saul, L.K.1
Jordan, M.I.2
-
100
-
-
1542537849
-
Combining phylogenetic and hidden Markov models in biosequence analysis
-
Siepel A, Haussler D. 2004. Combining phylogenetic and hidden Markov models in biosequence analysis. J. Comput. Biol. 11:413-28
-
(2004)
J. Comput. Biol
, vol.11
, pp. 413-428
-
-
Siepel, A.1
Haussler, D.2
-
101
-
-
36749051765
-
Latent variable modelling: A survey
-
Skrondal A, Rabe-Hesketh S. 2007. Latent variable modelling: a survey. Scand. J. Stat. 34:712-45
-
(2007)
Scand. J. Stat
, vol.34
, pp. 712-745
-
-
Skrondal, A.1
Rabe-Hesketh, S.2
-
104
-
-
33745909504
-
Probabilistic topic models
-
London: Laurence Erlbaum, ed. T Landauer, D McNamara, S Dennis, W Kintsch
-
Steyvers M, Griffiths T. 2006. Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning, ed. T Landauer, D McNamara, S Dennis, W Kintsch, pp. 424-40. London: Laurence Erlbaum
-
(2006)
Latent Semantic Analysis: A Road to Meaning
, pp. 424-440
-
-
Steyvers, M.1
Griffiths, T.2
-
105
-
-
0000629975
-
Cross-validatory choice and assessment of statistical predictions
-
Stone M. 1974. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B 36:111-47
-
(1974)
J. R. Stat. Soc. B
, vol.36
, pp. 111-147
-
-
Stone, M.1
-
106
-
-
77954201482
-
Hierarchical Bayesian nonparametric models with applications
-
ed. NL Hjort, C Holmes, P Müller, SG Walker, New York: Cambridge Univ. Press
-
Teh YW, Jordan MI. 2008. Hierarchical Bayesian nonparametric models with applications. In Bayesian Non-parametrics, ed. NL Hjort, C Holmes, P Müller, SG Walker, pp. 158-207. New York: Cambridge Univ. Press
-
(2008)
Bayesian Non-parametrics
, pp. 158-207
-
-
Teh, Y.W.1
Jordan, M.I.2
-
107
-
-
84979109755
-
The factorial analysis of human ability
-
Thomson G. 1939. The factorial analysis of human ability. Br. J. Educ. Psychol. 9:188-95
-
(1939)
Br. J. Educ. Psychol
, vol.9
, pp. 188-195
-
-
Thomson, G.1
-
108
-
-
0000296415
-
Multiple factor analysis
-
Thurstone LL. 1931. Multiple factor analysis. Psychol. Rev. 38:406-27
-
(1931)
Psychol. Rev
, vol.38
, pp. 406-427
-
-
Thurstone, L.L.1
-
110
-
-
0001560641
-
Fully exponential Laplace approximations to expectations and variances of nonpositive functions
-
Tierney L, Kass RE, Kadane JB. 1989. Fully exponential Laplace approximations to expectations and variances of nonpositive functions. J. Am. Stat. Assoc. 84:710-16
-
(1989)
J. Am. Stat. Assoc
, vol.84
, pp. 710-716
-
-
Tierney, L.1
Kass, R.E.2
Kadane, J.B.3
-
111
-
-
0038959172
-
Probabilistic principal component analysis
-
Tipping ME, Bishop CM. 1999. Probabilistic principal component analysis. J. R. Stat. Soc. B 61:611-22
-
(1999)
J. R. Stat. Soc. B
, vol.61
, pp. 611-622
-
-
Tipping, M.E.1
Bishop, C.M.2
-
112
-
-
0002331280
-
The future of data analysis
-
Tukey JW. 1962. The future of data analysis. Ann. Math. Stat. 33:1-67
-
(1962)
Ann. Math. Stat
, vol.33
, pp. 1-67
-
-
Tukey, J.W.1
-
113
-
-
14544284975
-
Highly structured models for spectral analysis in high-energy astrophysics
-
van Dyk DA, Kang H. 2004. Highly structured models for spectral analysis in high-energy astrophysics. Stat. Sci. 19:275-93
-
(2004)
Stat. Sci
, vol.19
, pp. 275-293
-
-
Van Dyk, D.A.1
Kang, H.2
-
114
-
-
65749118363
-
Graphical models, exponential families, and variational inference
-
Wainwright MJ, Jordan MI. 2008. Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1:1-305
-
(2008)
Found. Trends Mach. Learn
, vol.1
, pp. 1-305
-
-
Wainwright, M.J.1
Jordan, M.I.2
-
115
-
-
84877630966
-
Variational inference in nonconjugate models
-
Wang C, Blei DM. 2013. Variational inference in nonconjugate models. J. Mach. Learn. Res. 14:1005-1031
-
(2013)
J. Mach. Learn. Res
, vol.14
, pp. 1005-1031
-
-
Wang, C.1
Blei, D.M.2
-
117
-
-
0012131861
-
Variational approximations between mean field theory and the junction tree algorithm
-
Wiegerinck W. 2000. Variational approximations between mean field theory and the junction tree algorithm. Proc. Conf. Uncertain. Artif. Intell. 16:626-33
-
(2000)
Proc. Conf. Uncertain. Artif. Intell
, vol.16
, pp. 626-633
-
-
Wiegerinck, W.1
-
118
-
-
3242679207
-
A generalized mean field algorithm for variational inference in exponential families
-
Xing EP, Jordan MI, Russell S. 2003. A generalized mean field algorithm for variational inference in exponential families. Proc. Conf. Uncertain. Artif. Intell. 19:583-91
-
(2003)
Proc. Conf. Uncertain. Artif. Intell
, vol.19
, pp. 583-591
-
-
Xing, E.P.1
Jordan, M.I.2
Russell, S.3
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