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 (official publisher; peer reviewed)

  1. 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]

  2. 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), JMLR Workshop and Conference Proceedings, Vol. 52, pp. 426 - 473. [PDF JMLR]

  3. F. 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 Computional 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]

  4. 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]

  5. 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.

  6. 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.

  7. 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.

  8. 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]

  9. 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).

  10. 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

  11. 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)

  12. 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.

  13. 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.

  14. 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]

  15. 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)

  16. 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]

  17. 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.

  18. 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]

  19. 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]

  20. 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]

  21. 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]

  22. 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.

  23. 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.

  24. 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 IJAR38 (extended version).

  25. 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.

  26. 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.

  27. 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. 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.

  33. 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).

  34. 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.

  35. 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.
    (Also: technical report UU-CS-2000-16)

  36. 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.

  37. 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.
    (Also: technical report UU-CS-2000-18)

  38. 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.
    (Also: technical report UU-CS-1999-15)

  39. 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.
    (Also: technical report UU-CS-1999-23)

  40. 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 (not officially published, just printed or online; peer reviewed)

  1. 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.

  2. 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.

  3. 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.

  4. 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 (extended version).

  5. 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)

  6. 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.

  7. 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).

  8. 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.
    (preliminary version of IPMU12)

  9. 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.

  10. 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).

  11. 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.

  12. 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)

  13. 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).

  14. 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.

  15. 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)

  16. 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)

  17. 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)

  18. 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)

  19. 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)

  20. 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 (peer reviewed)

  1. 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)

  2. 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

  3. 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 Computional Models of Argument (COMMA), Frontiers in Artificial Intelligence and Applications, Vol. 266, IOS Press, pp. 479 - 480.

  4. 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.

  5. 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.

  6. 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)

  7. 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.

  8. 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.

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

  10. 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. 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.

  2. 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, pp. 1-2.

Journal contributions

Articles (peer reviewed)

  1. 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)

  2. 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)]

  3. 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]

  4. 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

  5. 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)

  6. 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)

  7. 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.

  8. 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)

  9. 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)

  10. 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.

  11. 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)

  12. 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.

  13. 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)

  14. 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)

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

  16. 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 Proceedings

  1. S. Renooij, H.J.M. Tabachneck-Schijf, S.M. Mahoney (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 (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 (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

See the technical report page.


Various reports

  1. S. Renooij (2014).
    Report of PGM 2014'. BNVKI -- AIABN Blog, to appear.

  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).


(anyone who would like a paper copy: please contact me)