dr. S. (Silja) Renooij
S.Renooij@uu.nl
Gegenereerd op 2018-07-23 19:57:53


Strategic themes / Focus areas
Involved in the following study programme(s)
Scientific expertise
Probabilistic graphical models (PGMs)
sensitivity analysis
qualitative probabilistic networks (qpns)
probability elicitation
Bayesian networks (not: Bayesian statistics)
Gegenereerd op 2018-07-23 19:57:53
Curriculum vitae

See my detailed homepage.

Gegenereerd op 2018-07-23 19:57:53

A list of all my publications can be found on my personal webpage.

All publications
  2018
Fernandez Ropero, R.M., Renooij, S. & van der Gaag, L.C. (2018). Discretizing environmental data for learning Bayesian-network classifiers. Ecological Modelling, 368, (pp. 391 - 403).
Wieten, G.M., Bex, F.J., van der Gaag, L.C., Prakken, H. & Renooij, S. (25.02.2018). Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments. In Bart Verheij & Marco Wiering (Eds.), The 29th Benelux Conference on Artificial Intelligence (BNAIC 2017) (pp. 32-45) (14 p.). Cham: Springer.
  2017
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, (pp. 475-494).
Bolt, J.H. & Renooij, S. (2017). Structure-based categorisation of Bayesian network parameters. In A. Antonucci, L. Cholvy & O. Papini (Eds.), Proceedings of the Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) (pp. 83-92).
  2016
Vlek, Charlotte, Prakken, H., Renooij, S. & Verheij, B. (2016). A method for explaining Bayesian networks for legal evidence with scenarios. Artificial Intelligence and Law, 24 (3), (pp. 285-324) (40 p.).
Verheij, B., Bex, F.J., Timmer, S.T., Vlek, Charlotte s., Meyer, J.J.C., Renooij, S. & Prakken, H. (2016). Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning.
Verheij, Bart, Bex, F.J., Timmer, S.T., Vlek, Charlotte s., Meyer, J.J.C., Renooij, S. & Prakken, H. (2016). Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. Law, Probability and Risk, 15 (1), (pp. 35) (70 p.).
Renooij, S. (2016). Evidence evaluation: a study of likelihoods and independence. In Alessandro Antonucci, Giorgio Corani & Cassio P. de Campos (Eds.), Proceedings of the Eighth International Conference on Probabilistic Graphical Models (pp. 426-473).
Bolt, J.H., De Bock, Jasper & Renooij, S. (2016). Exploiting Bayesian network sensitivity functions for inference in credal networks. Proceedings of the Twenty-Second European Conference on Artificial Intelligence (ECAI) (pp. 646-654). IOS Press.
Bex, F.J. & Renooij, S. (2016). From arguments to constraints on a Bayesian network. In Baroni, Gordon, Scheffler & Stede (Eds.), Proceedings of the Sixth International Conference on Computional Models of Argument (pp. 95-106) (12 p.). IOS Press.
Renooij, S. (2016). Special Issue on the Seventh Probabilistic Graphical Models Conference (PGM 2014). International Journal of Approximate Reasoning, 68, (pp. 88-90).
  2015
Renooij, S. () Chair 31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 Amsterdam (12.07.2015 - 16.07.2015) Tutorial chair
Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S. & Verheij, Bart (2015). A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. In T. Sichelman & K. Atkinson (Eds.), Proceedings of the Fifteenth International Conference on Artificial Intelligence and Law (ICAIL) (pp. 109-118). ACM Press.
Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S. & Verheij, Bart (10.12.2015). Capturing Critical Questions in Bayesian Network Fragments. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (pp. 173-176) (4 p.).
Vlek, Charlotte s., Prakken, H., Renooij, S. & Verheij, Bart (2015). Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Law (ICAIL) (pp. 128-137). New York: ACM Press.
Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S. & Verheij, Bart (2015). Demonstration of a Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. Demo abstract; demo of software used in ICAIL 2015 paper 'A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation'.
Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S. & Verheij, Bart (2015). Explaining Bayesian Networks using Argumentation. In Sebastien Destercke & Thierry Denoeux (Eds.), Proceedings of the Thirteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) (pp. 83 - 92). Springer.
Timmer, S.T., Meyer, J.J.C., Prakken, H., Renooij, S. & Verheij, Bart (10.12.2015). Explaining Legal Bayesian Networks Using Support Graphs. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (pp. 121-130) (10 p.).
Meekes, Michelle, Renooij, S. & van der Gaag, L.C. (2015). Relevance of Evidence in Bayesian Networks. In Sebastien Destercke & Thierry Denoeux (Eds.), Proceedings of the Thirteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) (pp. 366 - 375). Springer.
Vlek, Charlotte s., Prakken, H., Renooij, S. & Verheij, Bart (2015). Representing the quality of crime scenarios in a Bayesian network. In A. Rotolo (Eds.), Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (pp. 131-140) (10 p.). Amsterdam: IOS Press.
Bolt, J.H. & Renooij, S. (2015). Robustness of multi-dimensional Bayesian network classifiers. Proceedings of the 27th Benelux Artificial Intelligence Conference Hasselt, Belgium.
Renooij, Silja & Broersen, Jan (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, (pp. 1-2) (2 p.). S. Renooij en J. Broersen zijn guesteditors van de betreffende special issue van het tijdschrift..
  2014
Timmer, Sjoerd, Meyer, John-Jules Charles, Prakken, Hendrik, Renooij, Silja & Verheij, Bart (2014). A Tool for the Generation of Arguments from Bayesian Networks. In Simon Parsons, Nir Oren, Chris Reed & Frederico Cerutti (Eds.), Computational Models of Argument. Proceedings of COMMA 2014 (pp. 479-480). Amsterdam etc: IOS Press, (extended abstract of demo).
Vlek, Charlotte s., Prakken, Hendrik, Renooij, Silja & Verheij, Bart (2014). Building Bayesian networks for legal evidence with narratives: a case study evaluation. Artificial Intelligence and Law, 22 (4), (pp. 375-421).
Renooij, S. (2014). Co-variation for sensitivity analysis in Bayesian networks: Properties, consequences and alternatives. International Journal of Approximate Reasoning, 55 (4), (pp. 1022-1042) (21 p.).
Timmer, Sjoerd, Prakken, Hendrik, Meyer, John-Jules Charles, Renooij, Silja & Verheij, Bart (2014). Extracting legal arguments from forensic Bayesian networks. In Rinke Hoekstra (Eds.), Legal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference (pp. 71-80). IOS Press.
Vlek, Charlotte s., Prakken, Hendrik, Renooij, Silja & Verheij, Bart (2014). Extracting scenarios from a Bayesian network as explanations for legal evidence. In Rinke Hoekstra (Eds.), Legal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference (pp. 103-112) (10 p.). Amsterdam etc: IOS Press.
Renooij, S. (2014 2015) Guest editor International Journal of Approximate Reasoning (Journal) Special issue of PGM 2014
Bolt, J.H. & Renooij, S. (17.09.2014). 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) (16 p.). Springer, Probabilistic Graphical Models.
Bolt, Janneke H. & Renooij, Silja (2014). Sensitivity of multi-dimensional Bayesian classifiers. In Torsten Schaub, Gerhard Friedrich & Barry O'Sullivan (Eds.), Proceedings of the 21st European Conference on Artificial Intelligence - (ECAI) (pp. 971-972). IOS Press.
  2013
Renooij, S. (2013 2014) Organiser 7th Conference on Probabilistic Graphical Models (PGM 2014) Utrecht (17.09.2014 - 19.09.2014)
Timmer, S.T., Meyer, J-J.Ch., 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) (8 p.). Delft: TU Delft Library, bnaic13.
Renooij, S. (2013 2014) Guest editor International Journal of Approximate Reasoning (Journal) Special Issue of ECSQARU 2013
Vlek, C.S., Prakken, H., Renooij, S. & Verheij, B. (10.06.2013). Modeling crime scenarios in a Bayesian network. In B Verheij (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence and Law (pp. 150-159) (10 p.). New York: ACM Press, International Conference on Artificial Intelligence and Law (ICAIL).
Vlek, C.S., Prakken, H., Renooij, S. & Verheij, B. (2013). Representing and evaluating legal narratives with subscenarios in a Bayesian Network. Proceedings of the Fourth Workshop on Computational Models of Narrative Dagstuhl, Workshop on Computational Models of Narrative - a satellite workshop of CogSci.
Renooij, S. () Editorial board member The Scientific World Journal (Journal) Probability and Statistics subject area
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 (Eds.), Legal Knowledge and Information Systems. JURIX 2013: The Twenty-sixth Annual Conference (pp. 145-154) (10 p.). Amsterdam: IOS Press, jurix13vlek.
  2012
Renooij, S. (2012 2013) Organiser 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty Utrecht (07.07.2013 - 10.07.2013)
Bertens, R., van der Gaag, L.C. & Renooij, S. (09.07.2012). 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) (10 p.). Heidelberg: Springer, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Renooij, S. (2012). Efficient sensitivity analysis in hidden Markov models. International Journal of Approximate Reasoning, 53 (9), (pp. 1397-1414) (18 p.).
van der Gaag, L.C., Renooij, S., Schijf, H.J.M., Elbers, A.R.W. & Loeffen, W.L.A. (09.07.2012). 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) (10 p.). Heidelberg: Springer, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Renooij, S. (19.09.2012). 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) (8 p.). Granada: DECSAI Publications, European Workshop on Probabilistic Graphical Models.
  2011
Bertens, R., Renooij, S. & van der Gaag, L.C. (03.11.2011). 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) (8 p.). Gent, Benelux Conference on Artificial Intelligence.
  2010
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) (8 p.). Helsinki.
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) (8 p.). Helsinki.
Renooij, S. () Editorial board member Journal of Artificial Intelligence Research (Journal)
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. Proceedings of the 22nd Benelux Conference on Artificial Intelligence Luxembourg.
  2009
Renooij, S. (2009) Participant 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009) Verona (01.07.2009 - 03.07.2009) When in doubt ... be indecisive
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. (23.07.2009). Aligning Bayesian Network Classifiers with Medical Contexts. In P Perner (Eds.), Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 787-801) (15 p.). Berlin/Heidelberg: Springer, MLDM 2009.
van Kouwen, F.A., Renooij, S. & Schot, P.P. (2009). Inference in Qualitative Probabilistic Networks revisited. International Journal of Approximate Reasoning, 50 (5), (pp. 708-720) (13 p.).
van der Gaag, L.C., Renooij, S., Steeneveld, W. & Hogeveen, H. (01.07.2009). 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) (12 p.). Berlin Heidelberg: Springer, ECSQARU 2009.
Agosta, John Mark, Almond, Russell, Buede, Dennis M., Druzdzel, Marek J., Goldsmith, Judy & Renooij, Silja (2009). Workshop summary: Seventh annual workshop on Bayes applications. In Leon Bottou & Michael Littman (Eds.), Proceedings of the 26th Annual International Conference on Machine Learning (ICML) (pp. 163). Madison, Wisconsin, USA: omnipress.
  2008
Renooij, S. (2008) Participant 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008) Helsinki (09.07.2008 - 12.07.2008)
Renooij, S. (2008) Chair 6th UAI Bayesian Modelling Applications Workshop Helsinki (09.07.2008)
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) (8 p.). Hirtshals, Workshop on Probabilistic Graphical Models.
Renooij, S. & van der Gaag, L.C. (2008). Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial Intelligence, 172 (12-13), (pp. 1470-1494) (25 p.).
Renooij, S. & van der Gaag, L.C. (2008). Evidence and scenario sensitivities in naive Bayesian classifiers. International Journal of Approximate Reasoning, 49 (2), (pp. 398-416) (19 p.).
  2007
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, 7 (13).
Witteman, C.L.M., Renooij, S. & Koele, P. (2007). Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales. SPUDM (Subjective Probability, Utility and Decision Making) 2007 Symposium on Assessing clinical thinking and decision processes: Overview and comparative assessment across disciplines.
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) (22 p.). Berlin: Springer.
  2006
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) (8 p.). Prague, Czech Republic, RvdG06a.
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) (8 p.). Prague, Czech Republic, vdGRG06.
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) (11 p.). Amsterdam, The Netherlands: Elsevier, vdGR06 WAOA 2006.
  2005
Renooij, S. & van der Gaag, L.C. (27.07.2005). 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) (8 p.). Corvallis, OR: AUAI Press, Twenty-First Conference on Uncertainty in Artificial Intelligence.
Bolt, J.H., van der Gaag, L.C. & Renooij, S. (2005). Introducing situational signs in qualitative probabilistic networks. International Journal of Approximate Reasoning, 38, (pp. 333-354) (21 p.).
  2004
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) (8 p.). Arlington: 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) (8 p.). Groningen, Netherlands, R04 ISSN: 1568-7805.
van der Gaag, L.C. & Renooij, S. (2004). On the sensitivity of probabilistic networks to test reliability. Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1675-1682) (8 p.).
Bolt, J.H., van der Gaag, L.C. & Renooij, S. (2004). The practicability of situational signs for QPNs. Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1691-1698) (8 p.). Perugia, Italy.
  2003
Witteman, C.L.M. & Renooij, S. (2003). Evaluation of a verbal-numerical probability scale. International Journal of Approximate Reasoning, 33 (2), (pp. 117-131) (15 p.). WR03.
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) (12 p.). Berlin: Springer, pagina 113-124.
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) (5 p.). Berlin: 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) (8 p.). San Francisco, California: Morgan Kaufmann Publishers, pagina 73-80.
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) (13 p.). Berlin: Springer, Presented at the Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) in Aalborg Denmark, July 2003.
  2002
Renooij, S. (2002). Bookreview Qualitative Methods for Reasoning under Uncertainty. Artificial Intelligence in Medicine, 26 (3), (pp. 305-308) (4 p.).
Renooij, S., van der Gaag, L.C. & Parsons, S.D. (2002). Context-specific sign-propagation in qualitative probabilistic networks. Artificial Intelligence, 140, (pp. 207-230) (24 p.).
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) (8 p.). San Francisco, USA: Morgan Kaufman Publishers.
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), (pp. 123-148) (26 p.).
Renooij, S., van der Gaag, L.C. & Parsons, S.D. (2002). Propagation of multiple observations in QPNs revisited. In F. van Harmelen (Eds.), Proceedings of the Fifteenth European Conference on Artificial Intelligence (pp. 665-669) (5 p.). Amsterdam, the Netherlands: IOS Press.
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) (8 p.). Leuven, Belgium.
  2001
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) (8 p.). San Francisco, U.S.A.: Morgan Kaufmann Publishers.
Renooij, S., Parsons, S. & van der Gaag, L.C. (2001). Context-specific sign-propagation in qualitative probabilistic networks. In B. Nebel (Eds.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 667-672) (6 p.). San Francisco, California, U.S.A.: Morgan Kaufmann Publishers.
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) (8 p.). Amsterdam, The Netherlands: Universiteit van Amsterdam.
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) (5 p.). Berlin, Germany: Springer.
Renooij, S. (2001). Probability elicitation for belief networks: issues to consider. Knowledge Engineering Review, 16 (3), (pp. 255-269) (15 p.).
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) (12 p.). Amsterdam, The Netherlands: IOS Press.
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) (11 p.). Berlin: Springer.
  2000
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) (5 p.). Amsterdam: IOS Press.
Renooij, S. & van der Gaag, L.C. (2000). Exploiting non-monotonic influences in qualitative belief networks. Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (pp. 1285-1290) (6 p.). Madrid, Spain.
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) (8 p.). San Francisco, California: Morgan Kaufmann Publishers.
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) (8 p.). Tilburg: Tilburg University.
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) (5 p.). Menlo Park, California: AAAI Press.
  1999
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) (7 p.). San Francisco, CA: Morgan Kaufmann Publishers.
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) (7 p.). Maastricht.
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) (8 p.). San Fransisco, CA: Morgan Kaufmann Publishers.
Renooij, S. & Witteman, C.L.M. (1999). Talking probabilities: Communicating probabilistic information with words and numbers. International Journal of Approximate Reasoning, 22 (3), (pp. 169-194) (26 p.).
Renooij, S. & Witteman, C.L.M. (1999). Talking probabilities: communicating probalistic information with words and numbers. International Journal of Approximate Reasoning, 22 (3), (pp. 169-194) (26 p.).
  1998
Renooij, S. & van der Gaag, L.C. (1998). Decision making in qualitative influence diagrams. In D.J. Cook (Eds.), Proceedings of the Eleventh International FLAIRS Conference (pp. 410-414) (5 p.). Menlo Park, California, U.S.A..
  1997
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) (10 p.). Antwerp, Belgium: University of Antwerp.
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Gegenereerd op 2018-07-23 19:57:54

More details can be found on my research page or project page.

Project:
PROBAS: Probabilistic decision-making based on Arguments and Scenarios
01.12.2016 to 01.12.2020
General project description 

Bayesian networks (BNs) provide decision support in complex investigative domains where uncertainty plays a role, such as medicine, forensics and risk assessment. Yet, BNs are only sparsely used in practice. In data-poor domains, they have to be manually constructed, which is too time-consuming to support pressing decisions. Furthermore, few domain experts have the mathematical background to build a BN, a graph representing dependencies among variables with probability distributions over these variables. So despite the increased analytical power a BN could bring with respect to, for example, evidence aggregation or sensitivity analysis, many experts still use more qualitative concepts such as scenarios (stories, cases, timelines) and arguments (evidence graphs, ordered lists), which convey verbally expressed uncertainty ("strong evidence", "plausible scenarios").


If BNs are to be used in actual investigations, we need software tools and interfaces for BN construction that are engineered into the heart of the decision-making process. These tools should be based on familiar, more linguistically-oriented concepts such as arguments and stories, and complemented by algorithms intended to speed up and facilitate the BN-building process.

Role Co-promotor Funding
Utrecht University
Project members UU

Completed projects

Project:
Designing and Understanding Forensic Bayesian Networks with Arguments and Scenarios 01.06.2012 to 31.08.2016
General project description

Lucia de Berk found out first-hand: evidence based on statistics can easily lead to errors. This project aims to help prevent this sort of error from occurring. The project's new approach is to link the successful statistical modelling technique of Bayesian networks to models that effectively dovetail legal argumentation and scenario construction in the legal world.

 
Role Co-promotor Funding
NWO grant: NWO Forensic Science programme
Project members UU
External project members:
  • C.S. Vlek MSc
  • dr. B Verheij
  • prof. dr. L.C. Verbrugge (Department of Artificial Intelligence
  • University of Groningen)
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An overview of my teaching duties can be found on my personal webpage.

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Additional functions and activities

Editorial Board Memberships

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Full name
dr. S. Renooij Contact details
Buys Ballotgebouw

Princetonplein 5
Room BBL-508
3584 CC  UTRECHT
The Netherlands


Phone number (direct) +31 30 253 9266
Phone number (department) +31 30 253 9251
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Gegenereerd op 2018-07-23 19:57:54
Last updated 23.08.2017