A list of all my publications can be found on my personal webpage.
Publications
2023
Scholarly publications
Onnes, A., Renooij, S., & Dastani, M. (2023). Normative Monitoring Using Bayesian Networks: Defining a Threshold for Conflict Detection. In Z. Bouraoui, & S. Vesic (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 17th European Conference, ECSQARU 2023, Proceedings (Vol. 14294, pp. 149–159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14294 LNAI). Springer LNCS. https://doi.org/10.1007/978-3-031-45608-4_12
van Leeuwen, L., Verheij, B., Verbrugge, R., & Renooij, S. (2023). Evaluating Methods for Setting a Prior Probability of Guilt. In G. Sileno, J. Spanakis, & G. V. Dijck (Eds.), Legal Knowledge and Information Systems (pp. 63-72). (Frontiers in Artificial Intelligence and Applications; Vol. 379). IOS Press. https://doi.org/10.3233/FAIA230946
Steging, C., Renooij, S., & Verheij, B. (2023). Improving Rationales with Small, Inconsistent and Incomplete Data. In G. Sileno, J. Spanakis, & G. V. Dijck (Eds.), Legal Knowledge and Information Systems (pp. 53-62). ( Frontiers in Artificial Intelligence and Applications; Vol. 379). IOS Press. https://doi.org/10.3233/FAIA230945
Steging, C., Renooij, S., & Verheij, B. (2023). Taking the Law More Seriously by Investigating Design Choices in Machine Learning Prediction Research. CEUR Workshop Proceedings, 3441, 49-59.
https://dspace.library.uu.nl/bitstream/handle/1874/431780/paper6.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/431780/paper6.pdf?sequence=1
Leeuwen, van, L., Verheij, B., Verbrugge, R., & Renooij, S. (2023). Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios. In 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference (pp. 323-332). (19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference). ACM Press. https://doi.org/10.1145/3594536.3595125
Steging, C., Renooij, S., Verheij, B., & Bench-Capon, T. (2023). Arguments, rules and cases in law: Resources for aligning learning and reasoning in structured domains. Argument and Computation, 14(2), 235-243. https://doi.org/10.3233/AAC-220017
Valero-Leal, E., Bielza, C., Larranaga, P., & Renooij, S. (2023). Efficient search for relevance explanations using MAP-independence in Bayesian networks. International Journal of Approximate Reasoning, 160, Article 108965. https://doi.org/10.1016/j.ijar.2023.108965
Onnes, A., Dastani, M., & Renooij, S. (2023). Bayesian network conflict detection for normative monitoring of black-box systems. In Proceedings of the Thirty-Sixth International FLAIRS Conference (Vol. 36). (Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS). Florida Online Journals. https://doi.org/10.32473/flairs.36.133240
2022
Scholarly publications
Steging, C., Renooij, S., & Verheij, B. (2022). Discovering the Rationale of Decisions: Extended Abstract. In S. Schlobach, M. Pérez-Ortiz, & M. Tielman (Eds.), HHAI2022: Augmenting Human Intellect: Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence (pp. 255-257). (Frontiers in Artificial Intelligence and Applications; Vol. 354). IOS Press. https://doi.org/10.3233/FAIA220208
Renooij, S. (2022). Relevance for robust Bayesian network MAP-explanations. In A. Salmeron, & R. Rumi (Eds.), Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR (pp. 13-24). (Proceedings of Machine Learning Research; Vol. 186). MLResearchPress. https://proceedings.mlr.press/v186/renooij22a.html
Wieten, R., Bex, F., Prakken, H., & Renooij, S. (2022). Deductive and abductive argumentation based on information graphs. Argument and Computation, 13(1), 49-91. https://doi.org/10.3233/aac-200539
2021
Scholarly publications
Steging, C., Renooij, S., & Verheij, B. (2021). Discovering the rationale of decisions: experiments on aligning learning and reasoning. (pp. 1-21). arXiv. https://arxiv.org/abs/2105.06758
Koopman, T., & Renooij, S. (2021). Persuasive contrastive explanations. 1-6. Paper presented at XLoKR 2021. https://xlokr21.ai.vub.ac.be/
Koopman, T., & Renooij, S. (2021). Persuasive contrastive explanations for Bayesian networks. In J. Vejnarová, & N. Wilson (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 16th European Conference, ECSQARU 2021, Prague, Czech Republic, September 21–24, 2021, Proceedings (1 ed., pp. 229-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12897). Springer Cham. https://doi.org/10.1007/978-3-030-86772-0_17
Steging, C., Renooij, S., & Verheij, B. (2021). Discovering the rationale of decisions: towards a method for aligning learning and reasoning. In A. Z. Wyner (Ed.), Proceedings of the 18th International Conference on Artificial Intelligence and Law, ICAIL 2021 (pp. 235-239). (Proceedings of the 18th International Conference on Artificial Intelligence and Law, ICAIL 2021). ACM Press. https://doi.org/10.1145/3462757.3466059
Steging, C., Renooij, S., & Verheij, B. (2021). Rationale discovery and explainable AI. In E. Schweighofer (Ed.), Legal Knowledge and Information Systems (pp. 225-234). (Frontiers in Artificial Intelligence and Applications; Vol. 346). IOS Press. https://doi.org/10.3233/FAIA210341
Wieten, R., Bex, F., Prakken, H., & Renooij, S. (2021). Information graphs and their use for Bayesian network graph construction. International Journal of Approximate Reasoning, 136, 249-280. https://doi.org/10.1016/j.ijar.2021.06.007
2020
Scholarly publications
van der Gaag, L. C., Renooij, S., & Facchini, A. (2020). Building causal interaction models by recursive unfolding. In M. Jaeger, & T. D. Nielsen (Eds.), International Conference on Probabilistic Graphical Models, 23-25 September 2020, Hotel Comwell Rebild Bakker, Skørping, Denmark (pp. 509-520). (Proceedings of Machine Learning Research; Vol. 138). MLResearchPress. http://proceedings.mlr.press/v138/van-der-gaag20a.html
Wieten, G. M., Bex, F. J., Prakken, H., & Renooij, S. (2020). Deductive and Abductive Reasoning with Causal and Evidential Information. In H. Prakken, S. Bistarelli, F. Santini, & C. Taticchi (Eds.), Computational Models of Argument: Proceedings of COMMA 2020 (pp. 383-394). (Frontiers in Artificial Intelligence and Applications; Vol. 326). IOS Press. https://doi.org/10.3233/FAIA200522
2019
Scholarly publications
Renooij, S., & van der Gaag, L. C. (2019). The hidden elegance of causal interaction models. In N. Ben Amor, B. Quost, & M. Theobald (Eds.), Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings (1 ed., pp. 38-51). (Lecture Notes in Computer Science ; Vol. 11940). Springer Cham. https://doi.org/10.1007/978-3-030-35514-2_4
Renooij, S., van der Gaag, L. C., & Leray, P. (2019). On Intercausal Interactions in Probabilistic Relational Models. In J. De Bock, C. P. de Campos, G. de Cooman, E. Quaeghebeur, & G. Wheeler (Eds.), International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium (pp. 327-329). (Proceedings of Machine Learning Research; Vol. 103). MLResearchPress. http://proceedings.mlr.press/v103/renooij19a.html
Wieten, R., Bex, F., Prakken, H., & Renooij, S. (2019). Constructing Bayesian Network Graphs from Labeled Arguments. In G. Kern-Isberner, & Z. Ognjanović (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings (1 ed., pp. 99-110). (Lecture Notes in Computer Science; Vol. 11726). Springer Cham. https://doi.org/10.1007/978-3-030-29765-7_9
Wieten, G. M., Bex, F. J., Prakken, H., & Renooij, S. (2019). Supporting Discussions About Forensic Bayesian Networks Using Argumentation. In ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (pp. 143-152). Association for Computing Machinery (ACM). https://doi.org/10.1145/3322640.3326710
2018
Scholarly publications
Wieten, G. M., Bex, F. J., Prakken, H., & Renooij, S. (2018). Exploiting Causality in Constructing Bayesian Network Graphs from Legal Arguments. In M. Palmirani (Ed.), Legal Knowledge and Information Systems: JURIX 2018: The Thirty-first Annual Conference (pp. 151-160). (Frontiers in Artificial Intelligence and Applications; Vol. 313). IOS Press. https://doi.org/10.3233/978-1-61499-935-5-151
Renooij, S. (2018). Same-Decision Probability: threshold robustness and application to explanation. In V. Kratochvíl, & M. Studený (Eds.), Proceedings of the Ninth International Conference on Probabilistic Graphical Models (PGM) (pp. 368-379). (Proceedings of Machine Learning Research; Vol. 72). http://proceedings.mlr.press/v72/renooij18a/renooij18a.pdf
Fernandez Ropero, R. M., Renooij, S., & van der Gaag, L. C. (2018). Discretizing environmental data for learning Bayesian-network classifiers. Ecological Modelling, 368, 391-403. https://doi.org/10.1016/j.ecolmodel.2017.12.015
Wieten, G. M., Bex, F. J., van der Gaag, L. C., Prakken, H., & Renooij, S. (2018). Refining a heuristic for constructing Bayesian networks from structured arguments. In B. Verheij, & M. Wiering (Eds.), Artificial Intelligence: 29th Benelux Conference, BNAIC 2017, Groningen, The Netherlands, November 8–9, 2017, Revised Selected Papers (1 ed., pp. 32-45). (Communications in Computer and Information Science; Vol. 823). Springer Cham. https://doi.org/10.1007/978-3-319-76892-2_3
2017
Scholarly publications
Bolt, J. H., & Renooij, S. (2017). Structure-based categorisation of Bayesian network parameters. In A. Antonucci, L. Cholvy, & O. Papini (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings (pp. 83-92). (Lecture Notes in Computer Science ; Vol. 10369). Springer Cham. https://doi.org/10.1007/978-3-319-61581-3_8
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2017). A two-phase method for extracting explanatory arguments from Bayesian networks. International Journal of Approximate Reasoning, 80, 475-494. https://doi.org/10.1016/j.ijar.2016.09.002
2016
Scholarly publications
Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2016). A method for explaining Bayesian networks for legal evidence with scenarios. Artificial Intelligence and Law, 24(3), 285-324. https://doi.org/10.1007/s10506-016-9183-4
Bolt, J. H., De Bock, J., & Renooij, S. (2016). Exploiting Bayesian network sensitivity functions for inference in credal networks. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, & F. V. Harmelen (Eds.), ECAI 2016: Proceedings of the Twenty-Second European Conference on Artificial Intelligence (pp. 646-654). (Frontiers in Artificial Intelligence and Applications; Vol. 285). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-646
Bex, F. J., & Renooij, S. (2016). From arguments to constraints on a Bayesian network. In P. Baroni, T. F. Gordon, T. Scheffler, & M. Stede (Eds.), Computational Models of Argument (pp. 95-106). (Frontiers in Artificial Intelligence and Applications; Vol. 287). IOS Press. https://doi.org/10.3233/978-1-61499-686-6-95
Renooij, S. (2016). Evidence evaluation: a study of likelihoods and independence. In A. Antonucci, G. Corani, & C. P. Campos (Eds.), Conference on Probabilistic Graphical Models, 6-9 September 2016, Lugano, Switzerland (pp. 426-473). (Journal of Machine Learning Research Workshop and Conference Proceedings; Vol. 52). MLResearchPress. https://proceedings.mlr.press/v52/renooij16.html
Renooij, S. (2016). Special Issue on the Seventh Probabilistic Graphical Models Conference (PGM 2014). International Journal of Approximate Reasoning, 68, 88-90. https://doi.org/10.1016/j.ijar.2015.09.002
Verheij, B., Bex, F. J., Timmer, S. T., Vlek, C. S., Meyer, J. J. C., Renooij, S., & Prakken, H. (2016). Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. Law, Probability & Risk, 15(1), 35-70. https://doi.org/10.1093/lpr/mgv013
Other output
Verheij, B., Bex, F. J., Timmer, S. T., Vlek, C. S., Meyer, J. J. C., Renooij, S., & Prakken, H. (2016). Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. 192-193. Abstract from Benelux Conference on Artificial Intelligence, Amsterdam, Netherlands. http://www.cs.uu.nl/groups/IS/archive/henry/lpr2015.pdf
2015
Scholarly publications
Timmer, S., Meyer, J-J., Prakken, H., Renooij, S., & Verheij, B. (2015). A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. (Technical Report Series; No. UU-CS-2015-003). UU BETA ICS Departement Informatica.
https://dspace.library.uu.nl/bitstream/handle/1874/327006/2015_003.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/327006/2015_003.pdf?sequence=1
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2015). Representing the quality of crime scenarios in a Bayesian network. In A. Rotolo (Ed.), Legal Knowledge and Information Systems: JURIX 2015: The Twenty-eighth Annual Conference (pp. 131-140). (Frontiers in Artificial Intelligence and Applications; Vol. 279). IOS Press. https://ebooks.iospress.nl/volumearticle/41984
Bolt, J. H., & Renooij, S. (2015). Robustness of multi-dimensional Bayesian network classifiers. In Proceedings of the 27th Benelux Artificial Intelligence Conference
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining legal Bayesian networks using support graphs. In Legal Knowledge and Information Systems: JURIX 2015: The Twenty-eighth Annual Conference (pp. 121-130) http://www.cs.uu.nl/groups/IS/archive/henry/jurixST2015lang.pdf
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2015). Capturing Critical Questions in Bayesian Network Fragments. In Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (pp. 173-176) http://www.cs.uu.nl/groups/IS/archive/henry/jurixST2015kort.pdf
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining Bayesian Networks using Argumentation. In S. Destercke, & T. Denoeux (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings (1 ed., pp. 83-92). (Lecture Notes in Computer Science ; Vol. 9161). Springer Cham. https://doi.org/10.1007/978-3-319-20807-7_8
Meekes, M., Renooij, S., & van der Gaag, L. C. (2015). Relevance of Evidence in Bayesian Networks. In S. Destercke, & T. Denoeux (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings (1 ed., pp. 366-375). (Lecture Notes in Computer Science ; Vol. 9161). Springer Cham. https://doi.org/10.1007/978-3-319-20807-7_33
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2015). A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. In ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law (pp. 109-118). Association for Computing Machinery (ACM). https://doi.org/10.1145/2746090.2746093
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2015). Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. In ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law (pp. 128-137). Association for Computing Machinery (ACM). https://doi.org/10.1145/2746090.2746097
Renooij, S., & Broersen, J. (2015). Special Issue of the Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2013). International Journal of Approximate Reasoning, 58, 1-2. https://doi.org/10.1016/j.ijar.2015.01.002
Other output
Timmer, S. T., Meyer, J. J. C., Prakken, H., Renooij, S., & Verheij, B. (2015). Demonstration of a Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. 233-234. Abstract from 15th International Conference on Artificial Intelligence and Law, San Diego, United States. https://doi.org/10.1145/2746090.2750370
2014
Scholarly publications
Bolt, J., & Renooij, S. (2014). Sensitivity of Multi-dimensional Bayesian Classifiers. (Technical Report Series / Department of Information and Computing Sciences, Utrecht University; No. UU-CS-2014-024). UU BETA ICS Departement Informatica.
https://dspace.library.uu.nl/bitstream/handle/1874/306437/2014_024.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/306437/2014_024.pdf?sequence=1
Timmer, S., Meyer, J-J. C., Prakken, H., Renooij, S., & Verheij, B. (2014). A Tool for the Generation of Arguments from Bayesian Networks. In S. Parsons, N. Oren, C. Reed, & F. Cerutti (Eds.), Computational Models of Argument. Proceedings of COMMA 2014 (pp. 479-480). (Frontiers in Artificial Intelligence and Applications; Vol. 266). IOS Press. https://doi.org/10.3233/978-1-61499-436-7-479
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2014). Extracting scenarios from a Bayesian network as explanations for legal evidence. In R. Hoekstra (Ed.), Legal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference (pp. 103-112). (Frontiers in Artificial Intelligence and Applications; Vol. 271). IOS Press. https://doi.org/10.3233/978-1-61499-468-8-103
Timmer, S., Prakken, H., Meyer, J-J. C., Renooij, S., & Verheij, B. (2014). Extracting legal arguments from forensic Bayesian networks. In R. Hoekstra (Ed.), Legal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference (pp. 71-80). (Frontiers in Artificial Intelligence and Applications; Vol. 271). IOS Press. https://doi.org/10.3233/978-1-61499-468-8-71
https://dspace.library.uu.nl/bitstream/handle/1874/304319/jurix2014.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/304319/jurix2014.pdf?sequence=1
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2014). Building Bayesian networks for legal evidence with narratives: a case study evaluation. Artificial Intelligence and Law, 22(4), 375-421. https://doi.org/10.1007/s10506-014-9161-7
Bolt, J. H., & Renooij, S. (2014). Sensitivity of multi-dimensional Bayesian classifiers. In T. Schaub, G. Friedrich, & B. O'Sullivan (Eds.), Proceedings of the 21st European Conference on Artificial Intelligence: (ECAI) (pp. 971-972). (Frontiers in Artificial Intelligence and Applications ; Vol. 263). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-971
Renooij, S. (2014). Co-variation for sensitivity analysis in Bayesian networks: Properties, consequences and alternatives. International Journal of Approximate Reasoning, 55(4), 1022-1042. https://doi.org/10.1016/j.ijar.2013.07.004
Bolt, J. H., & Renooij, S. (2014). Local sensitivity of Bayesian networks to multiple simultaneous parameter shifts. In L. C. van der Gaag, & A. J. Feelders (Eds.), Proceedings of the Seventh European Workshop on Probabilistic Graphical Models (PGM) (pp. 65-80). (Lecture Notes in Computer Science ; Vol. 8754). Springer. https://doi.org/10.1007/978-3-319-11433-0
2013
Scholarly publications
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2013). Modeling crime scenarios in a Bayesian network. In B. Verheij (Ed.), Proceedings of the 14th International Conference on Artificial Intelligence and Law (pp. 150-159). ACM Press.
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2013). Unfolding crime scenarios with variations: a method for building a Bayesian network for legal narratives. In K. D. Ashley (Ed.), Legal Knowledge and Information Systems. JURIX 2013: The Twenty-sixth Annual Conference (pp. 145-154). (Frontiers in Artificial Intelligence and Applications; Vol. 259). IOS Press. http://www.cs.uu.nl/groups/IS/archive/henry/jurix13vlek.pdf
Timmer, S. T., Meyer, J-JC., Prakken, H., Renooij, S., & Verheij, B. (2013). Inference and attack in Bayesian networks. In K. Hindriks, M. de Weerdt, B. van Riemsdijk, & M. Warnier (Eds.), Proceedings of the 25th Benelux Conference on Artificial Intelligence (BNAIC 2013) (pp. 199-206). TU Delft Library. http://www.cs.uu.nl/groups/IS/archive/henry/bnaic2013.pdf
Vlek, C. S., Prakken, H., Renooij, S., & Verheij, B. (2013). Representing and evaluating legal narratives with subscenarios in a Bayesian Network. In Proceedings of the Fourth Workshop on Computational Models of Narrative (Open Access Series in Informatics)..
2012
Scholarly publications
Renooij, S. (2012). Generalised co-variation for sensitivity analysis in Bayesian networks. In A. Cano, M. Gomez-Olmedo, & T. D. Nielsen (Eds.), Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (pp. 267-274). DECSAI Publications. http://leo.ugr.es/pgm2012/proceedings/eproceedings/renooij_generalised.pdf
Bertens, R., van der Gaag, L. C., & Renooij, S. (2012). Discretisation effects in naive Bayesian networks. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, & R. R. Yager (Eds.), Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 161-170). (Communications in Computer and Information Sciences; Vol. 299). Springer.
van der Gaag, L. C., Renooij, S., Schijf, H. J. M., Elbers, A. R. W., & Loeffen, W. L. A. (2012). Experiences with eliciting probabilities from multiple experts. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, & R. R. Yager (Eds.), Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 151-160). (Communications in Computer and Information Sciences; Vol. 299). Springer.
Renooij, S. (2012). Efficient sensitivity analysis in hidden Markov models. International Journal of Approximate Reasoning, 53(9), 1397-1414. https://doi.org/10.1016/j.ijar.2012.06.003
2011
Scholarly publications
Renooij, S. (2011). Efficient sensitivity analysis in hidden Markov models. Department of Information and Computing Sciences, Utrecht University.
Bertens, R., Renooij, S., & van der Gaag, L. C. (2011). Towards being discrete in naive Bayesian networks. In P. De Causmaecker, J. Maervoet, T. Messelis, K. Verbeeck, & T. Vermeulen (Eds.), Proceedings of the Twenty-Third Benelux Conference on Artificial Intelligence (pp. 20-27). http://allserv.kahosl.be/bnaic2011/sites/default/files/bnaic2011_submission_31.pdf
Other output
Renooij, S. (2011). Efficient sensitivity analysis in HMMs. 425-426. Abstract from Benelux Conference on Artificial Intelligence, Gent, Belgium. http://allserv.kahosl.be/bnaic2011/sites/default/files/bnaic2011_submission_11.pdf
2010
Scholarly publications
van der Gaag, L. C., Renooij, S., Schijf, H. J. M., Elbers, A. R. W., & Loeffen, W. L. A. (2010). Probability Assessments from Multiple Experts: Qualitative Information is More Robust. In Proceedings of the 22nd Benelux Conference on Artificial Intelligence http://bnaic2010.uni.lu/Papers/Category A/Gaag.pdf
Renooij, S. (2010). Bayesian network sensitivity to arc-removal. In P. Myllymaki, T. Roos, & T. Jaakkola (Eds.), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (pp. 233-240). http://www.helsinki.fi/PGM2010/papers/renooij2.pdf
Renooij, S. (2010). Efficient sensitivity analysis in Hidden Markov models. In P. Myllymaki, T. Roos, & T. Jaakkola (Eds.), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (pp. 241-248). http://www.helsinki.fi/PGM2010/papers/renooij1.pdf
2009
Scholarly publications
Agosta, J. M., Almond, R., Buede, D. M., Druzdzel, M. J., Goldsmith, J., & Renooij, S. (2009). Workshop summary: Seventh annual workshop on Bayes applications. In L. Bottou, & M. Littman (Eds.), Proceedings of the 26th Annual International Conference on Machine Learning (ICML) (pp. 163). omnipress. https://doi.org/10.1145/1553374.1553540
van der Gaag, L. C., Renooij, S., Steeneveld, W., & Hogeveen, H. (2009). When in doubt ... be indecisive. In C. Sossai, & G. Chemello (Eds.), Proceedings of the Tenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 518-529). (Lecture Notes in Computer Science; Vol. 5590). Springer. https://doi.org/10.1007/978-3-642-02906-6_45
van der Gaag, L. C., Renooij, S., Feelders, A. J., de Groote, A. J., Eijkemans, M. J. C., Broekmans, F. J., & Fauser, B. C. J. M. (2009). Aligning Bayesian Network Classifiers with Medical Contexts. In P. Perner (Ed.), Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 787-801). Springer. https://doi.org/10.1007/978-3-642-03070-3_59
van Kouwen, F. A., Renooij, S., & Schot, P. P. (2009). Inference in Qualitative Probabilistic Networks revisited. International Journal of Approximate Reasoning, 50(5), 708-720. https://doi.org/10.1016/j.ijar.2008.12.001
2008
Scholarly publications
Renooij, S., (Tabachneck-)Schijf, H. J. M., & Mahoney, S. M. (2008). Foreword. CEUR Workshop Proceedings, 406.
Renooij, S., & van der Gaag, L. C. (2008). Evidence and Scenario Sensitivities in Naive Bayesian Classifiers. (UU-CS ed.) UU WINFI Informatica. http://www.cs.uu.nl/research/techreps/UU-CS-2008-040.html
Renooij, S., & van der Gaag, L. C. (2008). Evidence and scenario sensitivities in naive Bayesian classifiers. International Journal of Approximate Reasoning, 49(2), 398-416. https://doi.org/10.1016/j.ijar.2008.02.008
Renooij, S., & van der Gaag, L. C. (2008). Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial Intelligence, 172(12-13), 1470-1494. https://doi.org/10.1016/j.artint.2008.04.001
van der Gaag, L. C., Renooij, S., Feelders, A. J., de Groote, A. J., Eijkemans, M. J. C., Broekmans, F. J., & Fauser, B. C. J. M. (2008). Aligning Bayesian Network Classifiers With Medical Contexts. (UU-CS ed.) UU WINFI Informatica. http://www.cs.uu.nl/research/techreps/UU-CS-2008-015.html
Renooij, S., & van der Gaag, L. C. (2008). Discrimination and its sensitivity in probabilistic networks. In M. Jaeger, & T. D. Nielsen (Eds.), Proceedings of the Fourth Workshop on Probabilistic Graphical Models (pp. 241-248).
Professional publications
Renooij, S., Tabachneck-Schijf, H. J. M., & Mahoney, S. M. (2008). BMAW '08: Proceedings of the Sixth UAI Bayesian Modelling Applications Workshop. CEUR Workshop Proceedings. http://ceur-ws.org/Vol-406/
2007
Scholarly publications
Witteman, C. L. M., Renooij, S., & Koele, P. (2007). Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales. BMC medical informatics and decision making [E], 7(13). http://www.biomedcentral.com/1472-6947/7/13
van der Gaag, L. C., Renooij, S., & Coupé, V. M. H. (2007). Sensitivity analysis of probabilistic networks. In P. Lucas, J. A. Gamez, & A. Salmeron (Eds.), Advances in Probabilistic Graphical Models (pp. 103-124). (Studies in Fuzziness and Soft Computing; No. 213). Springer.
Other output
Witteman, C. L. M., Renooij, S., & Koele, P. (2007). Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales. Abstract from SPUDM (Subjective Probability, Utility and Decision Making) 2007 Symposium on Assessing clinical thinking and decision processes: Overview and comparative assessment across disciplines, Warsaw.
2006
Scholarly publications
Renooij, S., & van der Gaag, L. C. (2006). Evidence and scenario sensitivities in naive Bayesian classifiers. In M. Studeny, & J. Vomlel (Eds.), Proceedings of the Third European Workshop on Probabilistic Graphical Models (pp. 255-262).
van der Gaag, L. C., & Renooij, S. (2006). On the sensitivity of probabilistic networks to reliability characteristics. In B. Bouchon-Meunier, G. Coletti, & R. R. Yager (Eds.), Modern Information Processing: From Theory to Applications (pp. 395-405). Elsevier.
Renooij, S., & van der Gaag, L. C. (2006). Enhanced Qualitative Probabilistic Networks for Resolving Trade-offs. (UU-CS ed.) UU WINFI Informatica en Informatiekunde.
https://dspace.library.uu.nl/bitstream/handle/1874/24609/renooij_06_enhancedqualitative.pdf?sequence=2
https://dspace.library.uu.nl/bitstream/handle/1874/24609/renooij_06_enhancedqualitative.pdf?sequence=2
van der Gaag, L. C., Renooij, S., & Geenen, P. L. (2006). Lattices for studying monotonicity of Bayesian networks. In M. Studeny, & J. Vomlel (Eds.), Proceedings of the Third European Workshop on Probabilistic Graphical Models (pp. 99-106).
2005
Scholarly publications
Bolt, J. H., van der Gaag, L. C., & Renooij, S. (2005). Introducing situational signs in qualitative probabilistic networks. International Journal of Approximate Reasoning, 38, 333-354.
Renooij, S., & van der Gaag, L. C. (2005). Exploiting evidence-dependent sensitivity bounds. In F. Bacchus, & T. Jaakkola (Eds.), Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (pp. 485-492). AUAI Press.
2004
Scholarly publications
van der Gaag, L. C., & Renooij, S. (2004). Evidence-invariant sensitivity bounds. In M. Chickering, & J. Halpern (Eds.), Proceedings of the Twentieth Conference on Uncertainty in Artifical Intelligence. (pp. 479-486). AUAI Press.
Renooij, S. (2004). Forecast verification and the uncertain truth. In R. Verbrugge, N. Taatgen, & L. Schomaker (Eds.), Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 275-282).
van der Gaag, L. C., & Renooij, S. (2004). On the sensitivity of probabilistic networks to test reliability. In Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1675-1682)
Bolt, J. H., van der Gaag, L. C., & Renooij, S. (2004). The practicability of situational signs for QPNs. In Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1691-1698).
Bolt, J. H., van der Gaag, L. C., & Renooij, S. (2004). Introducing Situational Signs in Qualitative Probabilistic Networks. (UU-CS ed.) Utrecht University: Information and Computing Sciences.
https://dspace.library.uu.nl/bitstream/handle/1874/18033/bolt_04_introducing_situational.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/18033/bolt_04_introducing_situational.pdf?sequence=1
2003
Scholarly publications
van der Gaag, L. C., & Renooij, S. (2003). Probabilistic networks as probabilistic forecasters. In M. Dojat, E. Keravnou, & P. Barahona (Eds.), Proceedings of the Ninth Conference on Artificial Intelligence in Medicine in Europe (pp. 294-298). (Lecture Notes in Artificial Intelligence; Vol. 2780). Springer.
Bolt, J. H., Renooij, S., & van der Gaag, L. C. (2003). Upgrading ambiguous signs in QPNs. In C. Meek, & U. Kjaerulff (Eds.), Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (pp. 73-80). Morgan Kaufmann Publishers. http://www.cs.uu.nl/groups/DSS/publications/qpn/upgradingambiguoussings2003.ps
Witteman, C. L. M., & Renooij, S. (2003). Evaluation of a verbal-numerical probability scale. International Journal of Approximate Reasoning, 33(2), 117-131.
Renooij, S., Parsons, S., & Pardieck, P. (2003). Using kappas as indicators of strength in qualitative probabilistic networks. In T. D. Nielsen, & N. L. Zhang (Eds.), Proceedings of the 7th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 87-99). (Lecture Notes in Computer Science; Vol. 2711). Springer.
Bolt, J. H., van der Gaag, L. C., & Renooij, S. (2003). Introducing situational influences in QPNs. In T. D. Nielsen, & N. L. Zhang (Eds.), Proceedings of the Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 113-124). (Lecture Notes in Artificial Intelligence; Vol. 2711). Springer. http://www.cs.uu.nl/groups/DSS/publications/qpn/introducingsituationalinfluences2003.ps
2002
Scholarly publications
Renooij, S., van der Gaag, L. C., & Parsons, S. D. (2002). Propagation of multiple observations in QPNs revisited. In F. van Harmelen (Ed.), Proceedings of the Fifteenth European Conference on Artificial Intelligence (pp. 665-669). (Frontiers in Artificial Intelligence and Applications; Vol. 77). IOS Press.
Renooij, S., van der Gaag, L. C., & Parsons, S. (2002). Context-specific Sign-propagation in Qualitative Probabilistic Networks. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-024.html
Renooij, S., van der Gaag, L. C., & Parsons, S. D. (2002). Context-specific sign-propagation in qualitative probabilistic networks. Artificial Intelligence, 140, 207-230.
Renooij, S., Parsons, S. D., & Pardieck, P. (2002). Using kappas as indicators of strength in QPNs. In H. Blockeel, & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 267-274).
Renooij, S. (2002). Bookreview Qualitative Methods for Reasoning under Uncertainty. Artificial Intelligence in Medicine, 26(3), 305-308.
van der Gaag, L. C., Renooij, S., Witteman, C. L. M., Aleman, B. M. P., & Taal, B. G. (2002). Probabilities for a probabilistic network: A case-study in oesophageal cancer. Artificial Intelligence in Medicine, 25(2), 123-148.
Renooij, S., & van der Gaag, L. C. (2002). From qualitative to quantitative probabilistic networks. In A. Darwiche, & N. Friedman (Eds.), Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (pp. 422-429). Morgan Kaufman Publishers.
2001
Scholarly publications
Renooij, S. (2001). Qualitative Approaches to Quantifying Probabilistic Networks. [Doctoral thesis 1 (Research UU / Graduation UU), Utrecht University]. Utrecht University.
van der Gaag, L. C., Renooij, S., Witteman, C. L. M., Aleman, B. M. P., & Taal, B. G. (2001). Probabilities for a probabilistic network: A case-study in Oesophageal Carcinoma. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2001-01.html
van der Gaag, L. C., & Renooij, S. (2001). Evaluation scores for probabilistic networks. In B. Kröse, M. de Rijke, G. Schreiber, & M. van Someren (Eds.), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (pp. 109-116). Universiteit van Amsterdam.
van der Gaag, L. C., & Renooij, S. (2001). Analysing sensitivity data from probabilistic networks. In J. Breese, & D. Koller (Eds.), Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (pp. 530-537). Morgan Kaufmann Publishers.
van der Gaag, L. C., Witteman, C. L. M., Renooij, S., & Egmont-Petersen, M. (2001). The effects of disregarding test-characteristics in probabilistic networks. In S. Quaglini, P. Barahona, & S. Andreassen (Eds.), Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe (pp. 188-198). (Lecture Notes in Computer Science; Vol. 2101). Springer.
Renooij, S. (2001). Probability elicitation for belief networks: issues to consider. The knowledge engineering review, 16(3), 255-269.
Prakken, H., & Renooij, S. (2001). Reconstructing causal reasoning about evidence: a case study. In B. Verheij, A. R. Lodder, R. P. Loui, & A. J. Muntjewerff (Eds.), Legal Knowledge and Information Systems. JURIX 2001: The Fourteenth Annual Conference (pp. 131-142). (Frontiers in Artificial Intelligence and Applications; Vol. 70). IOS Press.
Renooij, S., Parsons, S., & van der Gaag, L. C. (2001). Context-specific sign-propagation in qualitative probabilistic networks. In B. Nebel (Ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 667-672). Morgan Kaufmann Publishers.
van der Gaag, L. C., & Renooij, S. (2001). On the evaluation of probabilistic networks. In S. Quaglini, P. Barahona, & S. Andreassen (Eds.), Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe (pp. 457-461). (Lecture Notes in Computer Science; Vol. 2101). Springer.
2000
Scholarly publications
van der Gaag, L. C., Renooij, S., Aleman, B. M. P., & Taal, B. G. (2000). Evaluation of a probabilistic model for staging of oesophageal carcinoma. In A. Hasman, B. Blobel, J. Dudeck, R. Engelbrecht, G. Gell, & H. U. Prokosch (Eds.), Medical Infobahn for Europe: Proceedings of MIE2000 and GMDS2000 (pp. 772-776). (Studies in Health Technology and Informatics; Vol. 77). IOS Press.
Renooij, S., van der Gaag, L. C., Parsons, S., & Green, S. D. (2000). Pivotal pruning of trade-offs in QPNs. In C. Boutilier, & M. Goldszmidt (Eds.), Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (pp. 515-522). Morgan Kaufmann Publishers.
Renooij, S., van der Gaag, L. C., Parsons, S., & Green, L. A. (2000). Pivotal pruning of trade-offs in QPNs. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-18.html
van der Gaag, L. C., Renooij, S., Aleman, B. M. P., & Taal, B. G. (2000). Evaluation of a probabilistic model for staging of oesophageal carcinoma. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-16.html
Renooij, S., van der Gaag, L. C., Green, S. D., & Parsons, S. (2000). Zooming in on trade-offs in qualitative probabilistic networks. In J. Etheredge, & B. Manaris (Eds.), Proceedings of the Thirteenth International Florida Artificial Intelligence Research Symposium (pp. 303-307). AAAI Press.
Renooij, S., & van der Gaag, L. C. (2000). Exploiting non-monotonic influences in qualitative belief networks. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-17.html
Renooij, S., & van der Gaag, L. C. (2000). Exploiting non-monotonic influences in qualitative belief networks. In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (pp. 1285-1290).
Renooij, S., van der Gaag, L. C., & Parsons, S. (2000). Propagation of multiple observations in qualitative probabilistic networks. In A. van den Bosch, & H. Weigand (Eds.), Proceedings of the Twelfth Belgium-Netherlands Artificial Intelligence Conference (pp. 235-242). Tilburg University.
1999
Scholarly publications
Renooij, S., & Witteman, C. L. M. (1999). Talking probabilities: communicating probalistic information with words and numbers. International Journal of Approximate Reasoning, 22(3), 169-194. https://doi.org/10.1016/S0888-613X(99)00027-4
van der Gaag, L. C., Renooij, S., Witteman, C. L. M., & Aleman, B. M. P. (1999). How to elicit many probabilities. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1999-15.html
Renooij, S., & Witteman, C. L. M. (1999). Talking probabilities: Communicating probabilistic information with words and numbers. International Journal of Approximate Reasoning, 22(3), 169-194.
Renooij, S., & van der Gaag, L. C. (1999). Enhancing QPNs for trade-off resolution. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1999-23.html
Renooij, S., & van der Gaag, L. C. (1999). Exploiting non-monotonic influences in qualitative belief networks. In E. Postma, & M. Gyssens (Eds.), Proceedings of the Eleventh Belgium-Netherlands Conference on Artificial Intelligence (pp. 131-138).
van der Gaag, L. C., Renooij, S., Witteman, C. L. M., Aleman, B., & Taal, B. G. (1999). How to elicit many probabilities. In K. B. Laskey, & H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 647-654). Morgan Kaufmann Publishers.
Renooij, S., & Witteman, C. L. M. (1999). Talking probabilities: communicating probabilistic information with words and numbers. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1999-19.html
Renooij, S., & van der Gaag, L. C. (1999). Enhancing QPNs for trade-off resolution. In K. B. Laskey, & H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 559-566). Morgan Kaufmann Publishers.
1998
Scholarly publications
Renooij, S., & van der Gaag, L. C. (1998). Decision making in qualitative influence diagrams. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1998-03.html
Renooij, S., & van der Gaag, L. C. (1998). Decision making in qualitative influence diagrams. In D. J. Cook (Ed.), Proceedings of the Eleventh International FLAIRS Conference (pp. 410-414).
1997
Scholarly publications
Renooij, S., & van der Gaag, L. C. (1997). Decision making in qualitative influence diagrams. In K. van Marcke, & W. Daelemans (Eds.), Proceedings of the Ninth Dutch Conference on Artificial Intelligence (pp. 93-102). University of Antwerp.