| Year |
Citation |
Score |
| 2020 |
Chen Y, Randerson JT, Coffield SR, Foufoula-Georgiou E, Smyth P, Graff CA, Morton DC, Andela N, van der Werf GR, Giglio L, Ott LE. Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales. Journal of Advances in Modeling Earth Systems. 12: e2019MS001955. PMID 33042387 DOI: 10.1029/2019Ms001955 |
0.302 |
|
| 2020 |
Graff CA, Coffield SR, Chen Y, Foufoula-Georgiou E, Randerson JT, Smyth P. Forecasting Daily Wildfire Activity Using Poisson Regression Ieee Transactions On Geoscience and Remote Sensing. 58: 4837-4851. DOI: 10.1109/Tgrs.2020.2968029 |
0.344 |
|
| 2019 |
Kotzias D, Lichman M, Smyth P. Predicting Consumption Patterns with Repeated and Novel Events Ieee Transactions On Knowledge and Data Engineering. 31: 371-384. DOI: 10.1109/Tkde.2018.2832132 |
0.367 |
|
| 2019 |
Coffield SR, Graff CA, Chen Y, Smyth P, Foufoula-Georgiou E, Randerson JT. Machine learning to predict final fire size at the time of ignition International Journal of Wildland Fire. 28: 861. DOI: 10.1071/Wf19023 |
0.315 |
|
| 2017 |
Holsclaw T, Greene AM, Robertson AW, Smyth P. Bayesian nonhomogeneous Markov models via Pólya-Gamma data augmentation with applications to rainfall modeling The Annals of Applied Statistics. 11: 393-426. DOI: 10.1214/16-Aoas1009 |
0.392 |
|
| 2017 |
Galbraith C, Smyth P. Analyzing user-event data using score-based likelihood ratios with marked point processes Digital Investigation. 22: S106-S114. DOI: 10.1016/J.Diin.2017.06.009 |
0.356 |
|
| 2016 |
Holsclaw T, Greene AM, Robertson AW, Smyth P. A bayesian hidden markov model of daily precipitation over South and East Asia Journal of Hydrometeorology. 17: 3-25. DOI: 10.1175/Jhm-D-14-0142.1 |
0.383 |
|
| 2016 |
Arnesen P, Holsclaw T, Smyth P. Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences Technometrics. 58: 205-213. DOI: 10.1080/00401706.2015.1044118 |
0.332 |
|
| 2015 |
Gaut G, Steyvers M, Imel Z, Atkins D, Smyth P. Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. Ieee Journal of Biomedical and Health Informatics. PMID 26625437 DOI: 10.1109/Jbhi.2015.2503985 |
0.306 |
|
| 2015 |
Holsclaw T, Hallgren KA, Steyvers M, Smyth P, Atkins DC. Measurement Error and Outcome Distributions: Methodological Issues in Regression Analyses of Behavioral Coding Data. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors. PMID 26098126 DOI: 10.1037/Adb0000091 |
0.342 |
|
| 2014 |
Frank A, Smyth P, Ihler A. Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker Ieee Transactions On Signal Processing. 62: 2413-2423. DOI: 10.1109/Tsp.2014.2311962 |
0.311 |
|
| 2013 |
DuBois C, Butts CT, McFarland D, Smyth P. Hierarchical models for relational event sequences Journal of Mathematical Psychology. 57: 297-309. DOI: 10.1016/J.Jmp.2013.04.001 |
0.759 |
|
| 2013 |
Navaroli N, DuBois C, Smyth P. Modeling individual email patterns over time with latent variable models Machine Learning. 92: 431-455. DOI: 10.1007/S10994-013-5348-5 |
0.694 |
|
| 2012 |
Gretarsson B, O'Donovan J, Bostandjiev S, Höllerer T, Asuncion A, Newman D, Smyth P. Topicnets: Visual analysis of large text corpora with topic modeling Acm Transactions On Intelligent Systems and Technology. 3. DOI: 10.1145/2089094.2089099 |
0.769 |
|
| 2012 |
Henke D, Smyth P, Haffke C, Magnusdottir G. Automated analysis of the temporal behavior of the double Intertropical Convergence Zone over the east Pacific Remote Sensing of Environment. 123: 418-433. DOI: 10.1016/J.Rse.2012.03.022 |
0.371 |
|
| 2012 |
Rubin TN, Chambers A, Smyth P, Steyvers M. Statistical topic models for multi-label document classification Machine Learning. 88: 157-208. DOI: 10.1007/S10994-011-5272-5 |
0.53 |
|
| 2011 |
Steyvers M, Smyth P, Chemuduganta C. Combining background knowledge and learned topics. Topics in Cognitive Science. 3: 18-47. PMID 25164174 DOI: 10.1111/J.1756-8765.2010.01097.X |
0.395 |
|
| 2011 |
Bain CL, De Paz J, Kramer J, Magnusdottir G, Smyth P, Stern H, Wang CC. Detecting the ITCZ in instantaneous satellite data using spatiotemporal statistical modeling: ITCZ climatology in the East Pacific Journal of Climate. 24: 216-230. DOI: 10.1175/2010Jcli3716.1 |
0.391 |
|
| 2011 |
Asuncion AU, Smyth P, Welling M. Asynchronous distributed estimation of topic models for document analysis Statistical Methodology. 8: 3-17. DOI: 10.1016/J.Stamet.2010.03.002 |
0.785 |
|
| 2011 |
Greene AM, Robertson AW, Smyth P, Triglia S. Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model Quarterly Journal of the Royal Meteorological Society. 137: 347-359. DOI: 10.1002/Qj.788 |
0.354 |
|
| 2010 |
Kim S, Smyth P, Stern H. A Bayesian mixture approach to modeling spatial activation patterns in multisite fMRI data. Ieee Transactions On Medical Imaging. 29: 1260-74. PMID 20304727 DOI: 10.1109/Tmi.2010.2044045 |
0.363 |
|
| 2010 |
Liu Q, Lin KK, Andersen B, Smyth P, Ihler A. Estimating replicate time shifts using Gaussian process regression. Bioinformatics (Oxford, England). 26: 770-6. PMID 20147305 DOI: 10.1093/Bioinformatics/Btq022 |
0.321 |
|
| 2010 |
Scharenbroich L, Magnusdottir G, Smyth P, Stern H, Wang CC. A Bayesian framework for storm tracking using a hidden-state repres Monthly Weather Review. 138: 2132-2148. DOI: 10.1175/2009Mwr2944.1 |
0.4 |
|
| 2010 |
Rosen-Zvi M, Chemudugunta C, Griffiths T, Smyth P, Steyvers M. Learning author-topic models from text corpora Acm Transactions On Information Systems. 28. DOI: 10.1145/1658377.1658381 |
0.707 |
|
| 2010 |
Hutchins J, Ihler A, Smyth P. Probabilistic analysis of a large-scale urban traffic sensor data set Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5840: 94-114. DOI: 10.1007/978-3-642-12519-5_6 |
0.525 |
|
| 2008 |
Smyth P, Kirshner S. Comment on article by Rydén Bayesian Analysis. 3: 699-706. DOI: 10.1214/08-Ba326B |
0.625 |
|
| 2007 |
Camargo SJ, Robertson AW, Gaffney SJ, Smyth P, Ghil M. Cluster analysis of typhoon tracks. Part II: Large-scale circulation and ENSO Journal of Climate. 20: 3654-3676. DOI: 10.1175/Jcli4203.1 |
0.758 |
|
| 2007 |
Camargo SJ, Robertson AW, Gaffney SJ, Smyth P, Ghil M. Cluster analysis of typhoon tracks. Part I. General properties Journal of Climate. 20: 3635-3653. DOI: 10.1175/Jcli4188.1 |
0.761 |
|
| 2007 |
Ihler A, Hutchins J, Smyth P. Learning to detect events with Markov-modulated poisson processes Acm Transactions On Knowledge Discovery From Data. 1. DOI: 10.1145/1297332.1297337 |
0.618 |
|
| 2007 |
Kirshner S, Smyth P. Infinite mixtures of trees Acm International Conference Proceeding Series. 227: 417-423. DOI: 10.1145/1273496.1273549 |
0.673 |
|
| 2007 |
Hutchins J, Ihler A, Smyth P. Modeling count data from multiple sensors: A building occupancy model 2007 2nd Ieee International Workshop On Computational Advances in Multi-Sensor Adaptive Processing, Campsap. 241-244. DOI: 10.1109/CAMSAP.2007.4498010 |
0.492 |
|
| 2007 |
Ihler AT, Kirshner S, Ghil M, Robertson AW, Smyth P. Graphical models for statistical inference and data assimilation Physica D: Nonlinear Phenomena. 230: 72-87. DOI: 10.1016/J.Physd.2006.08.023 |
0.726 |
|
| 2007 |
Gaffney SJ, Robertson AW, Smyth P, Camargo SJ, Ghil M. Probabilistic clustering of extratropical cyclones using regression mixture models Climate Dynamics. 29: 423-440. DOI: 10.1007/S00382-007-0235-Z |
0.772 |
|
| 2006 |
Robertson AW, Kirshner S, Smyth P, Charles SP, Bates BC. Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland Quarterly Journal of the Royal Meteorological Society. 132: 519-542. DOI: 10.1256/Qj.05.75 |
0.67 |
|
| 2005 |
O'Madadhain J, Hutchins J, Smyth P. Prediction and ranking algorithms for event-based network data Acm Sigkdd Explorations Newsletter. 7: 23-30. DOI: 10.1145/1117454.1117458 |
0.358 |
|
| 2004 |
Robertson AW, Kirshner S, Smyth P. Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden Markov model Journal of Climate. 17: 4407-4424. DOI: 10.1175/Jcli-3216.1 |
0.669 |
|
| 2003 |
Cadez I, Heckerman D, Meek C, Smyth P, White S. Model-Based Clustering and Visualization of Navigation Patterns on a Web Site Data Mining and Knowledge Discovery. 7: 399-424. DOI: 10.1023/A:1024992613384 |
0.768 |
|
| 2003 |
Chudova D, Smyth P. Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework Data Mining and Knowledge Discovery. 7: 273-299. DOI: 10.1023/A:1024032204965 |
0.307 |
|
| 2002 |
Cadez IV, Smyth P, McLachlan GJ, McLaren CE. Maximum likelihood estimation of mixture densities for binned and truncated multivariate data Machine Learning. 47: 7-34. DOI: 10.1023/A:1013679611503 |
0.769 |
|
| 2002 |
Smyth P. Learning with mixture models: Concepts and applications Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2430: 529. |
0.342 |
|
| 2001 |
Ge X, Eppstein D, Smyth P. The distribution of loop lengths in graphical models for turbo decoding Ieee Transactions On Information Theory. 47: 2549-2553. DOI: 10.1109/18.945266 |
0.514 |
|
| 2001 |
Cadez IV, Smyth P. Model complexity, goodness of fit and diminishing returns Advances in Neural Information Processing Systems. |
0.757 |
|
| 2000 |
Smyth P. Data mining: Data analysis on a grand scale? Statistical Methods in Medical Research. 9: 309-327. PMID 11084711 DOI: 10.1177/096228020000900402 |
0.34 |
|
| 2000 |
Smyth P. Model selection for probabilistic clustering using cross-validated likelihood Statistics and Computing. 10: 63-72. DOI: 10.1023/A:1008940618127 |
0.41 |
|
| 2000 |
Cadez IV, Smyth P. On model selection and concavity for finite mixture models Ieee International Symposium On Information Theory - Proceedings. 323. |
0.767 |
|
| 1999 |
Smyth P, Ide K, Ghil M. Multiple Regimes in Northern Hemisphere Height Fields via MixtureModel Clustering* Journal of the Atmospheric Sciences. 56: 3704-3723. DOI: 10.1175/1520-0469(1999)056<3704:Mrinhh>2.0.Co;2 |
0.389 |
|
| 1999 |
Fayyad UM, Smyth P. Cataloging and Mining Massive Datasets for Science Data Analysis Journal of Computational and Graphical Statistics. 8: 589-610. DOI: 10.1080/10618600.1999.10474835 |
0.327 |
|
| 1999 |
Smyth P, Wolpert D. Machine Learning. 36: 59-83. DOI: 10.1023/A:1007511322260 |
0.416 |
|
| 1998 |
Burl MC, Asker L, Smyth P, Fayyad U, Perona P, Crumpler L, Aubele J. Machine Learning. 30: 165-194. DOI: 10.1023/A:1007400206189 |
0.314 |
|
| 1997 |
Smyth P, Heckerman D, Jordan MI. Probabilistic independence networks for hidden Markov probability models. Neural Computation. 9: 227-69. PMID 9117903 DOI: 10.1162/Neco.1997.9.2.227 |
0.429 |
|
| 1997 |
BRODLEY CE, SMYTH P. Statistics and Computing. 7: 45-56. DOI: 10.1023/A:1018557312521 |
0.315 |
|
| 1997 |
Glymour C, Madigan D, Pregibon D, Smyth P. Statistical themes and lessons for data mining Data Mining and Knowledge Discovery. 1: 11-28. DOI: 10.1023/A:1009773905005 |
0.36 |
|
| 1997 |
Langley P, Provan GM, Smyth P. Machine Learning. 29: 91-101. DOI: 10.1023/A:1007467927290 |
0.375 |
|
| 1997 |
Smyth P. Belief networks, hidden Markov models, and Markov random fields: A unifying view Pattern Recognition Letters. 18: 1261-1268. DOI: 10.1016/S0167-8655(97)01050-7 |
0.348 |
|
| 1996 |
Fayyad UM, Piatetsky-Shapiro G, Smyth P. From Data Mining to Knowledge Discovery in Databases Ai Magazine. 17: 37-54. DOI: 10.1609/Aimag.V17I3.1230 |
0.303 |
|
| 1996 |
Glymour C, Madigan D, Pregibon D, Smyth P. Statistical Inference and Data Mining Communications of the Acm. 39: 35-41. DOI: 10.1145/240455.240466 |
0.325 |
|
| 1994 |
Piatetsky-Shapiro G, Matheus C, Smyth P, Uthurusamy R. KDD–93: progress and challenges in knowledge discovery in databases Ai Magazine. 15: 77-82. DOI: 10.1609/Aimag.V15I3.1103 |
0.304 |
|
| 1994 |
Smyth P. Markov Monitoring with Unknown States Ieee Journal On Selected Areas in Communications. 12: 1600-1612. DOI: 10.1109/49.339929 |
0.326 |
|
| 1993 |
Zeng Z, Goodman RM, Smyth P. Learning Finite State Machines With Self-Clustering Recurrent Networks Neural Computation. 5: 976-990. DOI: 10.1162/Neco.1993.5.6.976 |
0.307 |
|
| 1993 |
Goodman RM, Smyth P. Automated Induction of Rule-based Neural Networks from Databases Intelligent Systems in Accounting, Finance and Management. 2: 41-54. DOI: 10.1002/J.1099-1174.1993.Tb00033.X |
0.326 |
|
| 1992 |
Goodman RM, Higgins CM, Miller JW, Smyth P. Rule-Based Neural Networks for Classification and Probability Estimation Neural Computation. 4: 781-804. DOI: 10.1162/Neco.1992.4.6.781 |
0.31 |
|
| 1992 |
Smyth P, Goodman RM. An Information Theoretic Approach To Rule Induction From Databases Ieee Transactions On Knowledge and Data Engineering. 4: 301-316. DOI: 10.1109/69.149926 |
0.347 |
|
| 1992 |
Smyth P. Admissible stochastic complexity models for classification problems Statistics and Computing. 2: 97-104. DOI: 10.1007/Bf01889588 |
0.332 |
|
| 1990 |
Goodman RM, Smyth P. Decision tree design using information theory Knowledge Acquisition. 2: 1-19. DOI: 10.1016/S1042-8143(05)80020-2 |
0.315 |
|
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