Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
28014 | 216 | 33.2 | 43% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Cluster id | Level | Cluster label | #P |
---|---|---|---|
9 | 4 | COMPUTER SCIENCE, THEORY & METHODS//COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//COMPUTER SCIENCE, INFORMATION SYSTEMS | 1247339 |
34 | 3 | STATISTICS & PROBABILITY//STAT//COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 105938 |
1916 | 2 | DIRICHLET PROCESS//BAYESIAN NONPARAMETRICS//STATISTICS & PROBABILITY | 5867 |
28014 | 1 | MEAN FIELD VARIATIONAL BAYES//EXPECTATION PROPAGATION//VARIATIONAL BAYES | 216 |
Terms with highest relevance score |
rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|---|
1 | MEAN FIELD VARIATIONAL BAYES | authKW | 1272308 | 4% | 100% | 9 |
2 | EXPECTATION PROPAGATION | authKW | 1041481 | 9% | 39% | 19 |
3 | VARIATIONAL BAYES | authKW | 457418 | 11% | 13% | 24 |
4 | VARIATIONAL APPROXIMATE INFERENCE | authKW | 318076 | 1% | 75% | 3 |
5 | VARIATIONAL APPROXIMATION | authKW | 311303 | 6% | 16% | 14 |
6 | BAYESIAN PROBIT MODEL | authKW | 282735 | 1% | 100% | 2 |
7 | FIXED FORM VARIATIONAL BAYES | authKW | 282735 | 1% | 100% | 2 |
8 | MANIFOLD PRESERVING GRAPH REDUCTION | authKW | 282735 | 1% | 100% | 2 |
9 | NON CONJUGATE VARIATIONAL MESSAGE PASSING | authKW | 282735 | 1% | 100% | 2 |
10 | NONCONJUGATE VARIATIONAL MESSAGE PASSING | authKW | 282735 | 1% | 100% | 2 |
Web of Science journal categories |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | Computer Science, Artificial Intelligence | 7684 | 49% | 0% | 106 |
2 | Statistics & Probability | 3800 | 32% | 0% | 70 |
3 | Automation & Control Systems | 1524 | 18% | 0% | 38 |
4 | Robotics | 129 | 2% | 0% | 5 |
5 | Computer Science, Interdisciplinary Applications | 120 | 7% | 0% | 15 |
6 | Computer Science, Theory & Methods | 114 | 7% | 0% | 15 |
7 | Engineering, Electrical & Electronic | 92 | 14% | 0% | 31 |
8 | Mathematics, Interdisciplinary Applications | 74 | 5% | 0% | 10 |
9 | Mathematical & Computational Biology | 55 | 3% | 0% | 7 |
10 | Imaging Science & Photographic Technology | 16 | 1% | 0% | 3 |
Address terms |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | DCHA 42C01 | 141368 | 0% | 100% | 1 |
2 | INRIA FUTURS PROJECTS SELECT | 141368 | 0% | 100% | 1 |
3 | MACHINE PERCEPTMINIST EDUCSPEECH HEAR | 141368 | 0% | 100% | 1 |
4 | MATH BAT 425 | 141368 | 0% | 100% | 1 |
5 | SMILE CLIN | 141368 | 0% | 100% | 1 |
6 | SOE2 | 141368 | 0% | 100% | 1 |
7 | THEORET FDN SNN | 141368 | 0% | 100% | 1 |
8 | ASTROINFORMAT GRP | 70683 | 0% | 50% | 1 |
9 | BERLIN BIG DATA | 70683 | 0% | 50% | 1 |
10 | TELECOMS ELECT | 70683 | 0% | 50% | 1 |
Journals |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | JOURNAL OF MACHINE LEARNING RESEARCH | 116924 | 17% | 2% | 36 |
2 | BAYESIAN ANALYSIS | 31527 | 4% | 2% | 9 |
3 | STATISTICS AND COMPUTING | 6838 | 3% | 1% | 7 |
4 | AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS | 5812 | 2% | 1% | 5 |
5 | JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 5202 | 3% | 1% | 6 |
6 | NEURAL COMPUTATION | 4544 | 4% | 0% | 9 |
7 | ELECTRONIC JOURNAL OF STATISTICS | 4332 | 2% | 1% | 5 |
8 | COMPUTATIONAL STATISTICS & DATA ANALYSIS | 3448 | 5% | 0% | 11 |
9 | STATISTICAL SCIENCE | 3393 | 2% | 1% | 4 |
10 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 3065 | 5% | 0% | 10 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
---|---|---|---|---|
1 | ROHDE, D , WAND, MP , (2016) SEMIPARAMETRIC MEAN FIELD VARIATIONAL BAYES: GENERAL PRINCIPLES AND NUMERICAL ISSUES.JOURNAL OF MACHINE LEARNING RESEARCH. VOL. 17. ISSUE . P. - | 16 | 67% | 0 |
2 | MENICTAS, M , WAND, MP , (2015) VARIATIONAL INFERENCE FOR HETEROSCEDASTIC SEMIPARAMETRIC REGRESSION.AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS. VOL. 57. ISSUE 1. P. 119 -138 | 12 | 75% | 0 |
3 | LEE, CYY , WAND, MP , (2016) STREAMLINED MEAN FIELD VARIATIONAL BAYES FOR LONGITUDINAL AND MULTILEVEL DATA ANALYSIS.BIOMETRICAL JOURNAL. VOL. 58. ISSUE 4. P. 868 -895 | 11 | 69% | 0 |
4 | LEE, CYY , WAND, MP , (2016) VARIATIONAL METHODS FOR FITTING COMPLEX BAYESIAN MIXED EFFECTS MODELS TO HEALTH DATA.STATISTICS IN MEDICINE. VOL. 35. ISSUE 2. P. 165 -188 | 16 | 44% | 0 |
5 | TAN, LSL , ONG, VMH , NOTT, DJ , JASRA, A , (2016) VARIATIONAL INFERENCE FOR SPARSE SPECTRUM GAUSSIAN PROCESS REGRESSION.STATISTICS AND COMPUTING. VOL. 26. ISSUE 6. P. 1243 -1261 | 13 | 54% | 0 |
6 | ORMEROD, JT , WAND, MP , (2010) EXPLAINING VARIATIONAL APPROXIMATIONS.AMERICAN STATISTICIAN. VOL. 64. ISSUE 2. P. 140 -153 | 10 | 63% | 47 |
7 | CHALLIS, E , BARBER, D , (2013) GAUSSIAN KULLBACK-LEIBLER APPROXIMATE INFERENCE.JOURNAL OF MACHINE LEARNING RESEARCH. VOL. 14. ISSUE . P. 2239-2286 | 10 | 63% | 5 |
8 | RIIHIMAKI, J , JYLANKI, P , VEHTARI, A , (2013) NESTED EXPECTATION PROPAGATION FOR GAUSSIAN PROCESS CLASSIFICATION WITH A MULTINOMIAL PROBIT LIKELIHOOD.JOURNAL OF MACHINE LEARNING RESEARCH. VOL. 14. ISSUE . P. 75-109 | 8 | 73% | 4 |
9 | BUGBEE, BD , BREIDT, FJ , VAN DER WOERD, MJ , (2016) LAPLACE VARIATIONAL APPROXIMATION FOR SEMIPARAMETRIC REGRESSION IN THE PRESENCE OF HETEROSCEDASTIC ERRORS.JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS. VOL. 25. ISSUE 1. P. 225 -245 | 11 | 50% | 0 |
10 | VEHTARI, A , MONONEN, T , TOLVANEN, V , SIVULA, T , WINTHER, O , (2016) BAYESIAN LEAVE-ONE-OUT CROSS VALIDATION APPROXIMATIONS FOR GAUSSIAN LATENT VARIABLE MODELS.JOURNAL OF MACHINE LEARNING RESEARCH. VOL. 17. ISSUE . P. - | 13 | 42% | 0 |
Classes with closest relation at Level 1 |