| Causation, prediction, and search P Spirtes, C Glymour, R Scheines MIT press, 2001 | 11714 | 2001 |
| Learning Bayesian networks: The combination of knowledge and statistical data D Heckerman, D Geiger, DM Chickering Machine learning 20, 197-243, 1995 | 10138* | 1995 |
| Empirical analysis of predictive algorithms for collaborative filtering JS Breese, D Heckerman, C Kadie arXiv preprint arXiv:1301.7363, 2013 | 8483 | 2013 |
| A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD AE Renton, E Majounie, A Waite, J Simón-Sánchez, S Rollinson, ... Neuron 72 (2), 257-268, 2011 | 4905 | 2011 |
| A Bayesian approach to filtering junk e-mail M Sahami, S Dumais, D Heckerman, E Horvitz Learning for Text Categorization: Papers from the 1998 workshop 62, 98-105, 1998 | 2485 | 1998 |
| Inductive learning algorithms and representations for text categorization S Dumais, J Platt, D Heckerman, M Sahami Proceedings of the seventh international conference on Information and …, 1998 | 2477 | 1998 |
| Efficient control of population structure in model organism association mapping HM Kang, NA Zaitlen, CM Wade, A Kirby, D Heckerman, MJ Daly, E Eskin Genetics 178 (3), 1709-1723, 2008 | 1987 | 2008 |
| An MDP-based recommender system. G Shani, D Heckerman, RI Brafman, C Boutilier Journal of machine Learning research 6 (9), 2005 | 1479 | 2005 |
| FaST linear mixed models for genome-wide association studies C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman Nature methods 8 (10), 833-835, 2011 | 1332 | 2011 |
| CD8+ T-cell responses to different HIV proteins have discordant associations with viral load P Kiepiela, K Ngumbela, C Thobakgale, D Ramduth, I Honeyborne, ... Nature medicine 13 (1), 46-53, 2007 | 1247 | 2007 |
| Bayesian networks for data mining D Heckerman Data mining and knowledge discovery 1, 79-119, 1997 | 1197 | 1997 |
| The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users EJ Horvitz, JS Breese, D Heckerman, D Hovel, K Rommelse arXiv preprint arXiv:1301.7385, 2013 | 1190 | 2013 |
| Large-sample learning of Bayesian networks is NP-hard M Chickering, D Heckerman, C Meek Journal of Machine Learning Research 5, 1287-1330, 2004 | 1027 | 2004 |
| Technique which utilizes a probabilistic classifier to detect" junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set E Horvitz, DE Heckerman, ST Dumais, M Sahami, JC Platt US Patent 6,161,130, 2000 | 1015 | 2000 |
| Intelligent user assistance facility E Horvitz, JS Breese, DE Heckerman, SD Hobson, DO Hovel, AC Klein, ... US Patent 6,021,403, 2000 | 899 | 2000 |
| Dependency networks for inference, collaborative filtering, and data visualization D Heckerman, DM Chickering, C Meek, R Rounthwaite, C Kadie Journal of Machine Learning Research 1 (Oct), 49-75, 2000 | 822 | 2000 |
| Bayesian factor regression models in the “large p, small n” paradigm JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, ... Bayesian statistics 7, 733-742, 2003 | 771 | 2003 |
| Toward normative expert systems: Part i the pathfinder project DE Heckerman, EJ Horvitz, BN Nathwani Methods of information in medicine 31 (02), 90-105, 1992 | 719* | 1992 |
| Probabilistic interpretations for MYCIN's certainty factors D Heckerman Machine intelligence and pattern recognition 4, 167-196, 1986 | 703 | 1986 |
| Learning gaussian networks D Geiger, D Heckerman Uncertainty in Artificial Intelligence, 235-243, 1994 | 676 | 1994 |
