| Parameter learning for relational bayesian networks M Jaeger Proceedings of the 24th international conference on Machine learning, 369-376, 2007 | 353 | 2007 |
| Probabilistic reasoning in terminological logics M Jaeger Principles of Knowledge Representation and Reasoning, 305-316, 1994 | 243 | 1994 |
| Compiling relational Bayesian networks for exact inference M Chavira, A Darwiche, M Jaeger International Journal of Approximate Reasoning 42 (1), 4-20, 2006 | 160 | 2006 |
| Compiling Relational Bayesian Networks for Exact Inference M Jaeger, M Chavira, A Darwiche Proceedings of the Second European Workshop on Probabilistic Graphical Models, 2004 | 160* | 2004 |
| A theory of inductive query answering L De Raedt, M Jaeger, S Dan Lee, H Mannila Inductive databases and constraint-based data mining, 79-103, 2010 | 118 | 2010 |
| Complex probabilistic modeling with recursive relational Bayesian networks M Jaeger Annals of Mathematics and Artificial Intelligence 32, 179-220, 2001 | 74 | 2001 |
| Extending ProbLog with continuous distributions B Gutmann, M Jaeger, L De Raedt International Conference on Inductive Logic Programming, 76-91, 2010 | 68 | 2010 |
| Learning probabilistic automata for model checking H Mao, Y Chen, M Jaeger, TD Nielsen, KG Larsen, B Nielsen 2011 Eighth International Conference on Quantitative Evaluation of Systems …, 2011 | 62 | 2011 |
| Probabilistic decision graphs—combining verification and AI techniques for probabilistic inference M Jaeger International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2004 | 62 | 2004 |
| Relational bayesian networks: a survey M Jaeger Electronic Transactions in Artificial Intelligence 6, 60, 2002 | 56 | 2002 |
| Learning deterministic probabilistic automata from a model checking perspective H Mao, Y Chen, M Jaeger, TD Nielsen, KG Larsen, B Nielsen Machine Learning 105, 255-299, 2016 | 53 | 2016 |
| Liftability of probabilistic inference: Upper and lower bounds M Jaeger, G Van den Broeck Proceedings of the 2nd international workshop on statistical relational AI, 2012 | 49 | 2012 |
| Learning probabilistic decision graphs M Jaeger, JD Nielsen, T Silander International Journal of Approximate Reasoning 42 (1-2), 84-100, 2006 | 44 | 2006 |
| On the complexity of inference about probabilistic relational models M Jaeger Artificial Intelligence 117 (2), 297-308, 2000 | 44 | 2000 |
| Learning-Based Cleansing for Indoor RFID Data AI Baba, M Jaeger, H Lu, TB Pedersen, WS Ku, X Xie Proceedings of the 2016 International Conference on Management of Data, 925-936, 2016 | 39 | 2016 |
| Learning Markov decision processes for model checking H Mao, Y Chen, M Jaeger, TD Nielsen, KG Larsen, B Nielsen arXiv preprint arXiv:1212.3873, 2012 | 39 | 2012 |
| Reasoning about infinite random structures with relational Bayesian networks M Jaeger To appear in Proc. of KR 98, 1, 1998 | 35 | 1998 |
| On testing the missing at random assumption M Jaeger European Conference on Machine Learning, 671-678, 2006 | 34 | 2006 |
| Teaching stratego to play ball: optimal synthesis for continuous space MDPs M Jaeger, PG Jensen, KG Larsen, A Legay, S Sedwards, JH Taankvist International Symposium on Automated Technology for Verification and …, 2019 | 29 | 2019 |
| Ignorability for categorical data M Jaeger The annals of Statistics 33 (4), 1964-1981, 2005 | 28 | 2005 |
Kim G. LarsenDirector, Professor Aalborg UniversityVerified email at cs.aau.dk
