Personal

Silja Renooij

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Mark Twain:

"T
he time to begin writing an article is when you have finished it to your satisfaction. By that time you begin to clearly and logically perceive what it is you really want to say."

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Publications

This page lists my publications (also see output from Pure or Google Scholar).

Conference & Workshop contributions

Full Papers (formal, published; peer reviewed)

  1. A. Onnes, M.M. Dastani, R. Dobbe and S. Renooij (2025).
    Extending idioms for Bayesian network construction with qualitative constraints. In: M.-J. Lesot, S. Vieira, M.Z. Reformat, J. Paulo Carvalho, F. Batista, B. Bouchon-Meunir and R.R. Yager (editors), Proceedings of the Twentieth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024), Lecture Notes in Networks and Systems, vol. 1176, © Springer

  2. L. van Leeuwen, R. Verbrugge, B. Verheij and S. Renooij (2024).
    Building a stronger case: combining evidence and law in scenario-based Bayesian networks. In: F. Lorig, J. Tucker, A. Dahlgren Lindström, F. Dignum, P. Murukannaiah, A. Theodorou and P. Yolum (editors), Proceedings of the Third International Conference on Hybrid Human-Artificial Intelligence (HHAI), Frontiers in Artificial Intelligence and Applications, Vol. 386. IOS Press, pp. 291-299.
    [DOI 10.3233/FAIA240202 (Open Access)]

  3. C. Steging, S. Renooij and B. Verheij (2023).
    Improving Rationales with Small, Inconsistent and Incomplete Data. In: G. Sileno, J. Spanakis and G. van Dijck (editors), Proceedings of the 36th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, Vol. 379. IOS Press, pp. 53-62.
    [DOI 10.3233/FAIA230945 (Open Access)]

  4. L. van Leeuwen, B. Verheij, R. Verbrugge and S. Renooij (2023).
    Evaluating Methods for Setting a Prior Probability of Guilt. In: G. Sileno, J. Spanakis and G. van Dijck (editors), Proceedings of the 36th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, Vol. 379. IOS Press, pp. 63-72.
    [DOI 10.3233/FAIA230946 (Open Access)]

  5. A. Onnes, M.M. Dastani and S. Renooij (2023).
    Normative monitoring using Bayesian networks: defining a threshold for conflict detection. In: Z. Bouraoui and S. Vesic (editors), Proceedings of the Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023), LNCS vol 14294, Springer, Cham, pp. 149-159.
    [DOI 10.1007/978-3-031-45608-4_12]

  6. L. van Leeuwen, B. Verheij, R. Verbrugge and S. Renooij (2023).
    Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios. In: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 323-332.
    [DOI 10.1145/3594536.3595125 (Open Access)]

  7. A. Onnes, M.M. Dastani and S. Renooij (2023).
    Bayesian network conflict detection for normative monitoring of black-box systems. In: M. Franklin, S.A. Chun (editors), Proceedings of the Thirty-Sixth International FLAIRS Conference, Florida Online Journals Vol. 36.
    [DOI 10.32473/flairs.36.133240 (Open Access)]

  8. S. Renooij (2022).
    Relevance for robust Bayesian network MAP-explanations. In: A. Salmerón, R. Rumí(editors), Proceedings of the Eleventh International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research, Vol. 186, pp. 13-24.
    ☆ Selected for publication in IJAR 160 (extended version, jointly with E. Valero-Leal, C. Bielza and P. Larrañaga)

  9. C. Steging, S. Renooij and B. Verheij (2021).
    Rationale discovery and explainable AI. In: Erich Schweighofer (editor), Proceedings of the 34th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, Vol. 346. IOS Press, pp. 225-234.
    [DOI 10.3233/FAIA210341]

  10. T. Koopman and S. Renooij (2021).
    Persuasive contrastive explanations for Bayesian networks. In: J. Vejnarova and N. Wilson (editors), Proceedings of the Sixteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Computer Science, vol. 12897. Springer, Cham, pp. 229-242.
    [DOI 10.1007/978-3-030-86772-0_17]

  11. C. Steging, S. Renooij and B. Verheij (2021).
    Discovering the rationale of decisions: towards a method for aligning learning and reasoning. In: A.Z. Wyner (editor), Proceedings of the Eighteenth International Conference for Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 235-239.
    [DOI 10.1145/3462757.3466059] [Extended version: arXiv:2105.06758]

  12. L.C. van der Gaag, S. Renooij and A. Facchini (2020).
    Building causal interaction models by recursive unfolding. In: M. Jaeger and T.D. Nielsen (editors), Proceedings of the Tenth International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research, Vol. 138, pp. 509 - 520.
    ☆ Selected for publication in IJAR.

  13. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2020).
    Deductive and abductive reasoning with causal and evidential Information. In: H. Prakken, S. Bistarelli, F. Santini, C. Taticchi (editors), Proceedings of the International Conference on Computational Models of Argument (COMMA), Frontiers in Artificial Intelligence and Applications, Vol. 326, IOS Press, pp. 383 - 394.

  14. S. Renooij and L.C. van der Gaag (2019).
    The hidden elegance of causal interaction models. In: N. Ben Amor, B. Quost, M. Theobald (editors), Proceedings of the 13th international conference on Scalable Uncertainty Management (SUM), Lecture Notes in Artificial Intelligence, vol. 11940, Springer Verlag, pp. 38 - 51.

  15. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2019).
    Constructing Bayesian network graphs from labeled arguments In: G. Kern-Isberner and Z. Ognjanović (editors), Proceedings of the Fifteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Artificial Intelligence, vol. 11726, Springer Verlag, pp. 99 - 110.

  16. S. Renooij, L.C. van der Gaag and Ph. Leray (2019).
    On intercausal interactions in Probabilistic Relational Models . In: J. De Bock, C.P. de Campos, G. de Cooman, E. Quaeghebeur, Gregory Wheeler (editors), Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA), Proceedings of Machine Learning Research, Vol. 103, pp. 327 - 329 (short paper).

  17. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2019).
    Supporting discussions about forensic Bayesian networks using argumentation In: (editor), Proceedings of the Seventeenth International Conference for Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 143 - 152.

  18. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2018).
    Exploiting causality in constructing Bayesian network graphs from legal arguments In: M. Palmirani (editors), Proceedings of the 31st International Conference on Legal Knowledge and Information Systems (JURIX). Frontiers in Artificial Intelligence and Applications, vol. 313, IOS Press, pp. 151 - 160.
    [DOI 10.3233/978-1-61499-935-5-151]

  19. S. Renooij (2018).
    Same-Decision Probability: threshold robustness and application to explanation. In: V. Kratochíl, M. Studený (editors), Proceedings of the Ninth International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research, Vol.72, pp. 368 - 379.

  20. G.M. Wieten, F.J. Bex, L.C. van der Gaag, H. Prakken and S. Renooij (2018).
    Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments. In: B. Verheij, M. Wiering (editors), Post-Proceedings of the Twenty-Seventh Benelux Conference on Artificial Intelligence (BNAIC), Communications in Computer and Information Science, Vol 823, ©Springer, Cham, pp. 32-45.
    [PDF Publisher]

  21. J.H. Bolt, S. Renooij (2017).
    Structure-based categorisation of Bayesian network parameters. In: A. Antonucci, L. Cholvy, O. Papini (editors), Proceedings of the Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Computer Science, Vol. 10369, ©Springer, pp. 83-92.
    [PDF Publisher]

  22. S. Renooij (2016).
    Evidence evaluation: a study of likelihoods and independence. In: A. Antonucci, G. Corani, C.P. de Campos (editors), Proceedings of the Eighth International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research, Vol. 52, pp. 426 - 473. [PDF PMLR]

  23. F.J. Bex, S. Renooij (2016).
    From arguments to constraints on a Bayesian network. In: P. Baroni, Th.F. Gordon, T. Scheffler, M. Stede (editors), Proceedings of the Sixth International Conference on Computational Models of Argument (COMMA), Frontiers in Artificial Intelligence and Applications, Vol. 287, IOS Press, pp. 83 - 94.
    [DOI 10.3233/978-1-61499-686-6-95]

  24. J.H. Bolt, J. De Bock, S. Renooij (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. van Harmelen (editors), Proceedings of the Twenty-Second European Conference on Artificial Intelligence (ECAI), Frontiers in Artificial Intelligence and Applications, Vol. 285, IOS Press, pp. 646 - 654.
    [DOI 10.3233/978-1-61499-672-9-646]

  25. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2015).
    Representing the quality of crime scenarios in a Bayesian network In: (editors), Proceedings of the 28th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, vol. 279, IOS Press, pp. 131 - 140.

  26. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2015).
    Explaining legal Bayesian networks using support graphs In: (editors), Proceedings of the 28th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, vol. 279, IOS Press, pp. 121 - 130.

  27. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2015).
    Capturing critical questions in Bayesian network fragments In: (editors), Proceedings of the 28th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, vol. 279, IOS Press, pp. 173 - 176.

  28. M. Meekes, S. Renooij, L.C. van der Gaag (2015).
    Relevance of Evidence in Bayesian Networks In: S. Destercke, T. Denoeux (editors), Proceedings of the Thirteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Artificial Intelligence 9161, © Springer, pp. 366 - 375.
    [PDF Publisher]

  29. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2015).
    Explaining Bayesian Networks using Argumentation In: S. Destercke, T. Denoeux (editors), Proceedings of the Thirteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Artificial Intelligence 9161, © Springer, pp. 83 - 92.
    ☆ Selected for publication in IJAR (extended version).

  30. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2015).
    Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes In: (editor), Proceedings of the Fifteenth International Conference on Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 128 - 137.
    [DOI 10.1145/2746090.2746097]

  31. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2015). Best Student Paper Award ICAIL 2015(best student paper award)
    A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation In: (editor), Proceedings of the Fifteenth International Conference on Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 109 - 118.
    DOI 10.1145/2746090.2746093
    (Also: technical report UU-CS-2015-003)

  32. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2014).
    Extracting legal arguments from forensic Bayesian networks In: R. Hoekstra (editor), Proceedings of the 27th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, vol. 271, IOS Press, pp. 71 - 80.

  33. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2014).
    Extracting scenarios from a Bayesian network as explanations for legal evidence In: R. Hoekstra (editor), Proceedings of the 27th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications, vol. 271, IOS Press, pp. 103 - 112.

  34. J.H. Bolt, S. Renooij (2014).
    Local sensitivity of Bayesian networks to multiple simultaneous parameter shifts. In: L.C. van der Gaag, A.J. Feelders (editors), Proceedings of the Seventh European Workshop on Probabilistic Graphical Models (PGM), Lecture Notes in Artificial Intelligence, vol. 8754, © Springer-Verlag, pp. 65 - 80.
    [PDF Publisher]

  35. J.H. Bolt, S. Renooij (2014).
    Sensitivity of multi-dimensional Bayesian classifiers. In: T. Schaub, G. Friedrich, B. O'Sullivan (editors), Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI), Frontiers in Artificial Intelligence and Applications, Vol. 263, IOS Press, pp. 971 - 972.
    [DOI 10.3233/978-1-61499-419-0-971] (Proofs: technical report UU-CS-2014-024)

  36. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2013).
    Unfolding crime scenarios with variations: A method for building a Bayesian network for legal narratives In: K.D. Ashley (editor), Proceedings of the 26th International Conference on Legal Knowledge and Information Systems (JURIX), Frontiers in Artificial Intelligence and Applications Vol 259: Legal Knowledge and Information Systems, IOS Press, pp. 145-154.
    [DOI 10.3233/978-1-61499-359-9-145]

  37. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2013).
    Modeling crime scenarios in a Bayesian Network. In: B. Verheij (editor), Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 150-159.

  38. R. Bertens, L.C. van der Gaag, S. Renooij (2012).
    Discretisation effects in naive Bayesian networks. In: S.Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, R.R. Yager (editors), Proceedings of the Fourteenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Communications in Computer and Information Sciences, vol. 299, © Springer, Heidelberg, pp. 161-170.
    [PDF Publisher]

  39. L.C. van der Gaag, S. Renooij, H.J.M. Schijf, A.R. Elbers, W.L. Loeffen (2012).
    Experiences with Eliciting Probabilities from Multiple Experts. In: S.Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, R.R. Yager (editors), Proceedings of the Fourteenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Communications in Computer and Information Sciences, vol. 299, © Springer, Heidelberg, pp. 151-160.
    [PDF Publisher]

  40. L.C. van der Gaag, S. Renooij, W. Steeneveld, H.Hogeveen (2009).
    When in doubt ... be indecisive. In: C. Sossai, G. Chemello (editors), Proceedings of the Tenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Computer Science 5590, © Springer Verlag Berlin Heidelberg, pp. 518 - 529.
    [DOI 10.1007/978-3-642-02906-6_45]

  41. L.C. van der Gaag, S. Renooij, A. Feelders, A. de Groote, M.J.C. Eijkemans, F.J. Broekmans, B.C.J.M. Fauser (2009).
    Aligning Bayesian network classifiers with medical contexts. In: P.Perner (editor), Machine Learning and Data Mining in Pattern Recognition (MLDM), Lecture Notes in Computer Science 5632, © Springer Verlag Berlin Heidelberg, pp. 787 - 801.
    [DOI 10.1007/978-3-642-03070-3_59]

  42. S. Renooij, L.C. van der Gaag (2005).
    Exploiting evidence-dependent sensitivity bounds. In: F. Bacchus, T. Jaakkola (editors), Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, Corvallis, OR, pp. 485-492.
    [arXiv:1207.1357]

  43. S. Renooij, L.C. van der Gaag (2004).
    Evidence-invariant sensitivity bounds. In: M. Chickering, J. Halpern (editors), Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, Arlington, VA, pp. 479-486.
    [arXiv:1207.4170]

  44. J.H. Bolt, L.C. van der Gaag, S. Renooij (2003).
    Introducing situational influences in QPNs. In: T.D. Nielsen, N.L. Zhang (editors), Proceedings of the Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Computer Science 2711, © Springer Verlag, pp. 113 - 124.
    ☆ Selected for publication in IJAR 38 (extended version).

  45. S. Renooij, S. Parsons, P. Pardieck (2003).
    Using kappas as indicators of strength in qualitative probabilistic networks. In: T.D. Nielsen, N.L. Zhang (editors), Proceedings of the Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Lecture Notes in Computer Science 2711, © Springer Verlag, pp. 87-99.

  46. J.H. Bolt, S. Renooij, L.C. van der Gaag (2003).
    Upgrading ambiguous signs in QPNs. In: C. Meek, U. Kjærulff (editors), Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, pp. 73-80.
    [arXiv:1212.2445]

  47. L.C. van der Gaag, S. Renooij (2003).
    Probabilistic networks as probabilistic forecasters. In: M.Dojat, E. Keravnou, P. Barahona (editors), Proceedings of the Ninth Conference on Artificial Intelligence in Medicine in Europe (AIME), © Springer-Verlag, Berlin, Lecture Notes in Artificial Intelligence 2780, pp. 294-298.

  48. S. Renooij, L.C. van der Gaag, S. Parsons (2002).
    Propagation of multiple observations in QPNs revisited. In: F. van Harmelen (editor), Proceedings of the Fifteenth European Conference on Artificial Intelligence (ECAI), Vol 77, Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, pp. 665 - 669.

  49. S. Renooij, L.C. van der Gaag (2002).
    From qualitative to quantitative probabilistic networks. In: A. Darwiche, N. Friedman (editors), Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, pp. 422 - 429.
    [arXiv:1301.0596]

  50. L.C. van der Gaag, S. Renooij (2001).
    On the evaluation of probabilistic networks. In: S. Quaglini, P. Barahona, S. Andreassen (editors), Proceedings of the Eighth Conference on Artificial Intelligence in Medicine in Europe (AIME), © Springer-Verlag, Lecture Notes in Computer Science 2101, pp. 457-461.

  51. L.C. van der Gaag, C.L.M. Witteman, S. Renooij, M. Egmont-Petersen (2001).
    The effects of disregarding test-characteristics in probabilistic networks. In: S. Quaglini, P. Barahona, S. Andreassen (editors), Proceedings of the Eighth Conference on Artificial Intelligence in Medicine in Europe (AIME), © Springer-Verlag, Lecture Notes in Computer Science 2101, pp. 188-198.

  52. L.C. van der Gaag, S. Renooij (2001).
    Analysing sensitivity data from probabilistic networks. In: J. Breese, D. Koller (editors), Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, pp. 530-537.
    [arXiv:1301.2314]

  53. S. Renooij, S. Parsons, L.C. van der Gaag (2001).
    Context-specific sign-propagation in qualitative probabilistic networks. In: B. Nebel (editor), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), Morgan Kaufmann Publishers, San Francisco, pp. 667-672.
    ☆ Selected for publication in AI140 (extended version).

  54. H. Prakken, S. Renooij (2001).
    Reconstructing causal reasoning about evidence: A case study.  In: B. Verheij, A.R. Lodder, R.P. Loui, A. Muntjewerff (editors), Legal Knowledge and Information Systems. JURIX 2001: The Fourteenth Annual Conference, Vol. 70, Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, pp. 131-142.

  55. L.C. van der Gaag, S. Renooij, B.M.P Aleman, B.G. Taal (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 (editors), Medical Infobahn Europe: Proceedings of MIE2000 and GMDS2000, Studies in Health Technology and Informatics 77 IOS Press, Amsterdam, pp. 772-776.
    [DOI 10.3233/978-1-60750-921-9-772]
    (Also: technical report UU-CS-2000-16)

  56. S. Renooij, L.C. van der Gaag, S. Green, S. Parsons (2000).
    Zooming in on trade-offs in qualitative probabilistic networks. In: J. Etheredge, B. Manaris (editors), Proceedings of the Thirteenth International FLAIRS Conference, AAAI Press, Menlo Park, California, pp. 303-307.

  57. S. Renooij, L.C. van der Gaag, S. Parsons, S. Green (2000).
    Pivotal pruning of trade-offs in QPNs. In: G. Boutilier, M. Goldszmidt (editors), Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, California, pp. 515-522.
    [arXiv:1301.3889] (Also: technical report UU-CS-2000-18)

  58. L.C. van der Gaag, S. Renooij, C.L.M. Witteman, B.M.P. Aleman, B.G. Taal (1999).
    How to elicit many probabilities. In: K.B. Laskey, H. Prade (editors), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, California, pp. 647-654.
    [arXiv:1301.6745] (Also: technical report UU-CS-1999-15)

  59. S. Renooij, L.C. van der Gaag (1999).
    Enhancing QPNs for trade-off resolution. In: K.B. Laskey, H. Prade (editors), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, San Francisco, California, pp. 559-566.
    [arXiv:1301.6735] (Also: technical report UU-CS-1999-23)

  60. S. Renooij, L.C. van der Gaag (1998).
    Decision making in qualitative influence diagrams. In: D.J. Cook (editor), Proceedings of the Eleventh International FLAIRS Conference, AAAI Press, Menlo Park, California, pp. 410-414.
    (Also: technical report UU-CS-1998-03)

Full Papers (informal proceedings; peer reviewed)

  1. C. Steging, S. Renooij and B. Verheij (2024).
    Evaluating Legal Reasoning Capabilities of Generative Language Models. In: Q. Steenhuis, M. Hagan and H. Westermann (editors), Proceedings of the AI for Access to Justice Workshop at Jurix 2024, pp. - . CEUR Workshop Proceedings, vol. , CEUR-WS.org.

  2. J.H. Bolt, A. Berghuis, A. Hommersom, M. Lombaers, J.M.A. Pijnenborg and S. Renooij (2024).
    Best Short Paper Nominee HC@AIxIA 2024 (best short paper nominee)
    Bayesian networks in medicine: presenting query response uncertainty for decision support. In: F. Stella, M. Dragoni and F. Calimeri (editors), Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024), pp. - . CEUR Workshop Proceedings, vol. , CEUR-WS.org.

  3. C. Steging, S. Renooij and B. Verheij (2023).
    Taking the Law More Seriously by Investigating Design Choices in Machine Learning Prediction Research. In: F. Lagioia, J. Mumford, D. Odekerken, H. Westermann (editors), Proceedings of the Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023), pp. 49 - 59. CEUR Workshop Proceedings, vol. 3441, CEUR-WS.org.

  4. A. Onnes, M.M. Dastani and S. Renooij (2023).
    Bayesian network conflict detection for normative monitoring of black-box systems. In: P.K. Murukannaiah, T. Hirzle (editors), Proceedings of the Workshops at the Second International Conference on Hybrid Human-Artificial Intelligence, pp. 96 - 104. CEUR Workshop Proceedings, vol. 3456, CEUR-WS.org.
    (Previously published contribution (FLAIRS2023) presented at the Workshop on the Design of Responsible Intelligence)

  5. T. Koopman and S. Renooij (2021).
    Persuasive contrastive explanations. In: F. Baader, B. Bogaerts, G. Brewka, J. Hoffmann, T. Lukasiewicz, N. Potyka, F. Toni (editors) Proceedings of the Second Workshop on Explainable Logic-based Knowledge Representation (XLoKR) at KR 2021, 6 pages.
    (Extended abstract of ECSQARU 2021 paper)

  6. C. Steging, S. Renooij and B. Verheij (2021).
    Discovering the rationale of decisions: experiments on aligning learning and reasoning. In: M. Araszkiewicz, M. Atzmueller, G.J. Nalepa, B. Verheij, S. Bobek (editors) Proceedings of the Fourth International Workshop on Explainable and Responsible AI and Law (XAILA) at ICAIL 2021, 21 pages. CEUR Workshop Proceedings, CEUR-WS.org
    (extended version of ICAIL 2021)

  7. G.M. Wieten, F.J. Bex, L.C. van der Gaag, H. Prakken and S. Renooij (2017).
    Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments In: B. Verheij, M. Wiering (editors), Pre-Proceedings of the Twenty-Ninth Benelux Conference on Artificial Intelligence (BNAIC), pp. 32 - 45. Groningen, The Netherlands.

  8. J.H. Bolt, S. Renooij (2015).
    Robustness of Multi-dimensional Bayesian Network Classifiers. In: Proceedings of the Twenty-Seventh Benelux Conference on Artificial Intelligence (BNAIC), Hasselt, Belgium, 8 pages.

  9. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2013).
    Inference and Attack in Bayesian Networks. In: (editors), Proceedings of the Twenty-Fifth Benelux Conference on Artificial Intelligence (BNAIC), Delft: TU Delft Library, The Netherlands, pp. 199-206.

  10. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2013).
    Representing and evaluating legal narratives with subscenarios in a Bayesian Network. In: (editors), Proceedings of the Fourth Workshop on Computational Models of Narrative (CMN; a satellite workshop of CogSci) Open Access Series in Informatics, Dagstuhl, 18 pages.

  11. S. Renooij (2012).
    Generalised co-variation for sensitivity analysis in Bayesian networks. In: A. Cano, M. Gómez-Olmedo and T.D. Nielsen, (editors), Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (PGM), Granada, Spain, DECSAI Publications, pp. 267 - 274.
    ☆ Selected for publication in IJAR 55 (extended version).

  12. R. Bertens, S. Renooij, L.C. van der Gaag (2011).
    Towards being discrete in naive Bayesian networks. In: P. De Causmaecker, J. Maervoet, T. Messelis, K. Verbeeck, T. Vermeulen (editors), Proceedings of the Twenty-Third Benelux Conference on Artificial Intelligence (BNAIC), Ghent, Belgium, pp. 20 - 27.
    (preliminary version of IPMU12)

  13. S. Renooij (2010).
    Bayesian network sensitivity to arc-removal. In: P. Myllymäki, T. Roos and T. Jaakkola, (editors), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM), Helsinki, Finland, HIIT Publications 2010-2, pp. 233 - 240.

  14. S. Renooij (2010).
    Efficient sensitivity analysis in hidden Markov models. In: P. Myllymäki, T. Roos and T. Jaakkola, (editors), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM), Helsinki, Finland, HIIT Publications 2010-2, pp. 241 - 248.
    ☆ Selected for publication in IJAR53 (extended version).

  15. L.C. van der Gaag, S. Renooij, H.J.M. Schijf, A.R. Elbers, W.L. Loeffen (2010). Best Paper Nominee BNAIC 2010 (best paper nominee)
    Probability assessments from multiple experts: Qualitative information is more robust. In: Proceedings of the Twenty-Second Benelux Conference on Artificial Intelligence (BNAIC), Luxembourg, 8 pages.
    (preliminary version of IPMU12)

  16. S. Renooij, L.C. van der Gaag (2008).
    Discrimination and its sensitivity in probabilistic networks. In: Manfred Jaeger and Thomas D. Nielsen, (editors), Proceedings of the Fourth Workshop on Probabilistic Graphical Models (PGM), Hirtshals, Denmark, pp. 241 - 248.

  17. S. Renooij, L.C. van der Gaag (2006).
    Evidence and scenario sensitivities in naive Bayesian classifiers. In: M. Studený, J. Vomlel (editors), Proceedings of the Third Workshop on Probabilistic Graphical Models (PGM), Prague, pp. 255 - 262.
    ☆ Selected for publication in IJAR49 (extended version).

  18. L.C. van der Gaag, S. Renooij, P.L. Geenen (2006).
    Lattices for studying monotonicity of Bayesian networks. In: M. Studený, J. Vomlel (editors), Proceedings of the Third Workshop on Probabilistic Graphical Models (PGM), Prague, pp.99 - 106.

  19. J.H. Bolt, L.C. van der Gaag, S. Renooij (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 (IPMU), Perugia, pp. 1691-1698 (volume 3).
    (included in technical report UU-CS-2004-006 and IJAR38)

  20. L.C. van der Gaag, S. Renooij (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 (IPMU), Perugia, pp. 1675-1682 (volume 3).
    ☆ Selected for publication in MIP:FTA (adapted version).

  21. S. Renooij (2004).
    Forecast verification and the uncertain truth. In: R. Verbrugge, N. Taatgen and L. Schomaker (editors), Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), Groningen, pp. 275 - 282.

  22. S. Renooij, S. Parsons, P. Pardieck (2002). Best Paper Award BNAIC 2002(best paper award)
    Using kappas as indicators of strength in QPNs. In: H. Blockeel and M. Denecker (editors), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), Leuven, pp. 267-274.
    (preliminary version of ECSQARU03)

  23. L.C. van der Gaag, S. Renooij (2001). Best Paper Award BNAIC 2001(best paper award)
    Evaluation scores for probabilistic networks. In: B. Kröse, M. de Rijke, G. Schreiber and M. van Someren (editors), Proceedings of the Thirteenth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), Amsterdam, pp. 109-116.
    (preliminary version of AIME03)

  24. S. Renooij, L.C. van der Gaag (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 (IPMU), Madrid, pp. 1285-1290.
    (Also: technical report UU-CS-2000-17)

  25. S. Renooij, L.C. van der Gaag, S. Parsons (2000).
    Propagation of multiple observations in qualitative probabilistic networks. In: A. van den Bosch, H. Weigand (editors), Proceedings of the Twelfth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), Kaatsheuvel, pp. 235-242.
    (preliminary version of ECAI02)

  26. S. Renooij, L.C. van der Gaag (1999).
    Exploiting non-monotonic influences in qualitative belief networks. In: E. Postma, M. Gyssens (editors), Proceedings of the Eleventh Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), Maastricht, pp. 131-138.
    (preliminary version of IPMU00)

  27. S. Renooij, L.C. van der Gaag (1997).
    Decision making in qualitative influence diagrams. In: K. Van Marcke, W. Daelemans (editors), Proceedings of the Ninth Dutch Conference on Artificial Intelligence (NAIC), Antwerp, pp. 93-102.
    (preliminary version of FLAIRS98)

Abstracts only (max 2 pages; peer reviewed)

  1. C. Steging, S. Renooij and B. Verheij (2022).
    Discovering the rationale of decisions (Extended Abstract). In: S. Schlobach, M. Perez-Ortiz, M. Tielman (editors), Proceedings of the First International Conference on Hybrid Human-Machine Intelligence (HHAI), Frontiers in Artificial Intelligence and Applications, vol. 354, IOS Press, pp. 255-257.
    [DOI 10.3233/FAIA220208]

  2. B. Verheij, F.J. Bex, S.T. Timmer, C.S. Vlek, J.J.-Ch. Meyer, S. Renooij, H. Prakken (2016).
    Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. In: T. Bosse, B. Bredeweg (editors), Proceedings of the Twenty-Eighth Benelux Conference on Artificial Intelligence (BNAIC), Amsterdam, pp. 192 - 193.
    (abstract of LPR 15)

  3. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2015).
    Demonstration of a Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation In: (editor), Proceedings of the Fifteenth International Conference on Artificial Intelligence and Law (ICAIL), ACM Press, New York, pp. 233 - 234.
    [DOI 10.1145/2746090.2750370]

  4. S.T. Timmer, J.-J. Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2014).
    A tool for the generation of arguments from Bayesian networks. In: S. Parsons, N. Oren, C. Reed, F. Cerutti (editors), Proceedings of the Fifth International Conference on Computational Models of Argument (COMMA), Frontiers in Artificial Intelligence and Applications, Vol. 266, IOS Press, pp. 479 - 480.

  5. S.T. Timmer, J.-J. Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2014).
    Constructing arguments from Bayesian networks about forensic evidence. In: the Ninth International Conference on Forensic Inference and Statistics (ICFIS), Leiden, The Netherlands.

  6. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2014).
    Crime scenarios in a Bayesian network: modeling forensic evidence with narrative. In: the Ninth International Conference on Forensic Inference and Statistics (ICFIS), Leiden, The Netherlands.

  7. S. Renooij (2011).
    Efficient sensitivity analysis in HMMs. In: P. De Causmaecker, J. Maervoet, T. Messelis, K. Verbeeck, T. Vermeulen (editors), Proceedings of the Twenty-Third Benelux Conference on Artificial Intelligence (BNAIC), Ghent, Belgium, pp. 425 - 426.
    (abstract of PGM10)

  8. C.L.M. Witteman, S. Renooij, P. Koele (2007).
    Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales. Abstract for the SPUDM (Subjective Probability, Utility and Decision Making) 2007 Symposium on Assessing clinical thinking and decision processes: Overview and comparative assessment across disciplines, Warsaw.

  9. M. Egmont-Petersen, S. Renooij, L.C. van der Gaag (2001).
    From statistical pattern recognition to decision support using probabilistic networks - similarities and differences. Abstract for the 2001 Spring Meeting of the NVPHBV (Nederlandse Vereniging voor Patroonherkenning en Beeldverwerking /  Dutch Society for Pattern Recognition and Image Processing), Amsterdam.

  10. C.L.M. Witteman, S. Renooij (2001).
    A verbal-numerical probability scale. Abstract for SPUDM (Subjective Probability, Utility and Decision Making) 2001, Amsterdam.

  11. S. Renooij, C.L.M. Witteman (1999).
    Beslissen op basis van verbale en numerieke waarschijnlijkheden. Abstract in het supplement Zevende NVP Wintercongres, bij De Psychonoom, Nieuwsblad voor de Nederlandse Vereninging voor Psychonomie, jaargang 14, nr 3.

Proceedings Preface (NOT reviewed)

  1. P. Ericson, A. Dahlgren Lindström, A. Jonsson, S. Renooij, V. de Boer, R. Siebes, A. Lensen (2023)
    Heterodox methods for interpretable and efficient AI (HMIEAI). In: P. Ericson, A. Dahlgren Lindström, A. Jonsson, S. Renooij, V. de Boer, R. Siebes, A. Lensen (editors), Proceedings of the 1st Workshop on Heterodox methods for interpretable and efficient AI (HMIEAI 2022), Amsterdam, The Netherlands.
    [DOI 10.5281/zenodo.7740025]

  2. J.M. Agosta, R. Almond, D.M. Buede, M.J. Druzdzel, J. Goldsmith, S. Renooij (2009)
    Workshop summary: Seventh annual workshop on Bayes applications. In: L. Bottou, M.L. Littman (editors), Proceedings of the 26th Annual International Conference on Machine Learning (ICML'09), Montreal, Quebec, Canada, Omnipress, pp. 163.
    [DOI 10.1145/1553374.1553540]

  3. S. Renooij, H.J.M. Tabachneck-Schijf, S.M. Mahoney (2008)
    Foreword. In: S. Renooij, H.J.M. Tabachneck-Schijf, S.M. Mahoney (editors) BMAW '08. Proceedings of the Sixth UAI Bayesian Modelling Applications Workshop, Helsinki, Finland, CEUR Workshop Proceedings, vol. 406, pp. 1-2.

Journal contributions

Articles (peer reviewed)

  1. E. Valero-Leal, C. Bielza, P. Larrañaga and S. Renooij (2023).
    Efficient search for relevance explanations using MAP-independence in Bayesian networks. International Journal of Approximate Reasoning, vol 160, 108965.
    [DOI 10.1016/j.ijar.2023.108965]
    (Extended version of PGM2022, by invitation)

  2. C. Steging, S. Renooij, B. Verheij and T. Bench-Capon (2023).
    Arguments, rules and cases in law: Resources for aligning learning and reasoning in structured domains. Argument and Computation, vol 14, pp. 235-243.
    (Argument & Computation Community Resources (ACCR) Corner)
    [DOI 10.3233/AAC-220017]

  3. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2022).
    Deductive and abductive argumentation based on information graphs. Argument and Computation, vol 13, pp. 49 - 91.
    [DOI 10.3233/AAC-200539]

  4. G.M. Wieten, F.J. Bex, H. Prakken and S. Renooij (2021).
    Information graphs and their use for Bayesian network graph construction. International Journal of Approximate Reasoning, vol 136, pp. 249 - 280.
    [DOI 10.1016/j.ijar.2021.06.007]

  5. R.F. Ropero, S. Renooij, L.C. van der Gaag (2018).
    Discretizing environmental data for learning Bayesian-network classifiers. Ecological Modelling, 368, pp. 391 - 403.
    [DOI 10.1016/j.ecolmodel.2017.12.015]

  6. S.T. Timmer, J.-J.Ch. Meyer, H. Prakken, S. Renooij, B. Verheij (2017).
    A two-phase method for extracting explanatory arguments from Bayesian networks. International Journal of Approximate Reasoning 80, pp. 475 - 494.
    [DOI 10.1016/j.ijar.2016.09.002] (Extended version of ECSQARU2015, by invitation)

  7. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2016).
    A method for explaining Bayesian networks for legal evidence with scenarios. Artificial Intelligence and Law 24(3), pp. 285 - 324. DOI 10.1007/s10506-016-9183-4
    [Full-text (Springer Nature content sharing)]

  8. B. Verheij, F.J. Bex, S.T. Timmer, C.S. Vlek, J.J.-Ch. Meyer, S. Renooij, H. Prakken (2016).
    Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. Law, Probability & Risk 15(1), pp. 35 - 70.
    [DOI 10.1093/lpr/mgv013]

  9. C.S. Vlek, H. Prakken, S. Renooij, B. Verheij (2014).
    Building Bayesian networks for legal evidence with narratives: a case study evaluation. Artificial Intelligence and Law, 22(4), pp. 375 - 421.
    [DOI 10.1007/s10506-014-9161-7]

  10. S. Renooij (2014).
    Co-variation for sensitivity analysis in Bayesian networks: properties, consequences and alternatives. International Journal of Approximate Reasoning, vol 55(4), pp. 1022-1042.
    (Extended version of PGM12, by invitation)

  11. S. Renooij (2012).
    Efficient sensitivity analysis in hidden Markov models. International Journal of Approximate Reasoning, vol 53(9), pp. 1397-1414.
    (Extended version of PGM10, by invitation; also: extends on revised technical report UU-CS-2011-025)

  12. F.A. van Kouwen, S. Renooij, P. Schot (2009).
    Inference in qualitative probabilistic networks revisited. International Journal of Approximate Reasoning, vol 50(5), pp. 708-720.

  13. S. Renooij, L.C. van der Gaag (2008).
    Evidence and scenario sensitivities in naive Bayesian classifiers. International Journal of Approximate Reasoning, vol 49(2), pp. 398-416.
    (Extended version of PGM06, by invitation; also: technical report UU-CS-2008-040)

  14. S. Renooij, L.C. van der Gaag (2008).
    Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial Intelligence, vol 172(12-13), pp. 1470-1494.
    (Revised version of technical report UU-CS-2006-034; extends on ideas from UAI99)

  15. C.L.M. Witteman, S. Renooij, P. Koele (2007).
    Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales. BMC Medical Informatics and Decision Making 2007, 7:13.

  16. J.H. Bolt, L.C. van der Gaag, S. Renooij (2005).
    Introducing situational signs in qualitative probabilistic networks. International Journal of Approximate Reasoning, Special Issue, vol. 38(3), pp. 333-354.
    (Extended version of ECSQARU03 and IPMU04, by invitation; also: technical report UU-CS-2004-006)

  17. C.L.M. Witteman, S. Renooij (2003).
    Evaluation of a verbal-numerical probability scale.  International Journal of Approximate Reasoning, vol. 33(2), pp. 117-131.

  18. L.C. van der Gaag, S. Renooij, C.L.M. Witteman, B.M.P. Aleman, B.G. Taal (2002).
    Probabilities for a probabilistic network: A case-study in oesophageal cancer. Artificial Intelligence in Medicine, vol. 25 (2), pp. 123-148.
    (Also: technical report UU-CS-2001-01)

  19. S. Renooij, L.C. van der Gaag, S. Parsons (2002).
    Context-specific sign-propagation in qualitative probabilistic networks. Artificial Intelligence, vol. 140, pp. 207-230.
    (Extended version of IJCAI01, by invitation; also: technical report UU-CS-2002-024)

  20. S. Renooij (2001).
    Probability elicitation for belief networks: Issues to consider. Knowledge Engineering Review, vol. 16 (3), pp. 255-269.

  21. S. Renooij, C.L.M. Witteman (1999).
    Talking probabilities: communicating probabilistic information with words and numbers. International Journal of Approximate Reasoning, vol. 22, pp. 169-194.
    (Also: technical report UU-CS-1999-19 and CKI Preprint Series nr. 013, nov 99)

Editorials

  1. S. Renooij (2016).
    Special Issue on the Seventh Probabilistic Graphical Models Conference (PGM 2014). International Journal of Approximate Reasoning, vol. 68, pp. 88-90.

  2. S. Renooij, J.M. Broersen (2015).
    Special Issue of the Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2013). International Journal of Approximate Reasoning, vol. 58, pp. 1-2.

Book Reviews

  1. S. Renooij (2002).
    Book review of 'Qualitative Methods for Reasoning under Uncertainty' by Simon Parsons, MIT Press, 2001.  Artificial Intelligence in Medicine, vol. 26 (3), pp. 305-308.

Books

Chapters

  1. L.C. van der Gaag, S. Renooij, V.M.H. Coupé (2007).
    Sensitivity analysis of probabilistic networks. In: P. Lucas, J.A. Gamez and A. Salmerón (editors), Advances in Probabilistic Graphical Models, Springer Series: Studies in Fuzziness and Soft Computing , Vol. 213, pp. 103-124. ISBN: 978-3-540-68994-2
    (by invitation)

  2. L.C. van der Gaag, S. Renooij (2006).
    On the sensitivity of probabilistic networks to reliability characteristics. In: B. Bouchon-Meunier, G. Coletti and R.R. Yager (editors), Modern Information Processing: From Theory to Applications, © Elsevier B.V., pp. 395-405. ISBN 0-444-52075-9
    (Adapted version of IPMU04, by invitation)

Edited Volumes

Conference & Workshop Proceedings

  1. P. Ericson, A. Dahlgren Lindström, A. Jonsson, S. Renooij, V. de Boer, R. Siebes, A. Lensen (editors) (2023)
    Proceedings of the 1st Workshop on Heterodox methods for interpretable and efficient AI (HMIEAI 2022), Amsterdam, The Netherlands.

  2. S. Renooij, H.J.M. Tabachneck-Schijf, S.M. Mahoney (editors) (2008)
    BMAW '08. Proceedings of the Sixth UAI Bayesian Modelling Applications Workshop, Helsinki, Finland. CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-406/.

Journal Special Issues

  1. S. Renooij (editor) (2016)
    International Journal of Approximate Reasoning, vol. 68: Special Issue of the Seventh European Workshop on Probabilistic Graphical Models (PGM 2014).

  2. S. Renooij, J.M. Broersen (editors) (2015)
    International Journal of Approximate Reasoning, vol. 58: Special Issue of the Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2013).

Reports

Technical Reports

Various reports

  1. S. Renooij (2014).
    Report of 'The Seventh European Workshop on Probabilistic Graphical Models (PGM 2014)'. Announced in the BNVKI -- AIABN Blog of April 2, 2014.

  2. S. Renooij (2013).
    Report of the 'Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)". BNVKI -- AIABN Blog, September 27, 2013

  3. S. Renooij (2008).
    Report of the 'Sixth Bayesian Modelling Applications Workshop'. The Reasoner, vol. 2, nr 9, pp. 11-12. www.thereasoner.org

  4. S. Renooij (2004).
    Report of 'Session 6C: AI in Law and Medicine' at the 2004 BNAIC conference. Newsletter BNVKI, vol. 21, nr 6, pp. 130-131. ISSN: 1566-8266

  5. D. Sent and S. Renooij (2001).
    Report of 'Session 5A: AI and Medicine' at the 2001 BNAIC conference. Newsletter BNVKI, vol. 18, nr 6, p. 147.

  6. S. Renooij (1997).
    Verslag van de presentatie 'Defeasible argumentatie in juridisch redeneren' door Henry Prakken, in het kader van de SIKS AiO cursus 'Redeneervormen voor AI'. Nieuwsbrief NVKI, jaargang 14, nr 13, p. 77.

Theses

  1. S. Renooij (2001).
    Qualitative Approaches to Quantifying Probabilistic Networks.
    Ph.D. Thesis, Institute for Information and Computing Sciences,
    Utrecht University, The Netherlands.  ISBN 90-393-2644-4

  2. S. Renooij (1996).
    Qualitative Probabilistic Networks (the Binary case).
    MSc Thesis, Department of Computer Science, Utrecht University, The Netherlands. INF/SCR-96-24 (August 1996).