Bolt, J.H. & Gaag, L.C. van der (2012). Multi-dimensional Classification with Naive Bayesian Network Classifiers. In J.W.H.M. Uiterwijk, N. Roos & M.H.M. Wijnands (Eds.), Proceedings of the 24th Benelux Conference on Artificial Intelligence (pp. 27-34).
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Bertens, R., Gaag, L.C. van der & Renooij, S. (2012). Discretisation effects in naive Bayesian networks. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo & R.R. Yager (Eds.), Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 161-170). Heidelberg: Springer.
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Gaag, L.C. van der, Renooij, S., Schijf, H.J.M., Elbers, A.R.W. & Loeffen, W.L.A. (2012). Experiences with eliciting probabilities from multiple experts. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo & R.R. Yager (Eds.), Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 151-160). Heidelberg: Springer.
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Woudenberg, S.P.D. & Gaag, L.C. van der (2012). Intercausal Cancellation in Bayesian Networks. In J.W.H.M. Uiterwijk (Ed.), Proceedings of the 24th Benelux Conference on Artificial Intelligence (pp. 242-249).
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Gaag, L.C. van der, Schijf, H.J.M., Elbers, A.R.W. & Loeffen, W.L.A. (2012). Preserving Precision as a Guideline for Interface Design for Mathematical Models. In J.W.H.M. Uiterwijk, N. Roos & M.H.M. WIjnands (Eds.), Proceedings of the 24th Benelux Conference on Artificial Intelligence (pp. 107-114).
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Rietbergen, M.T. & Gaag, L.C. van der (2011). Attaining Monotonicity for Bayesian Networks. In W Liu (Ed.), Proceedings 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011) Lecture Notes in Computer Science (pp. 134-145). Belfast: Springer.
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Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2011). Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever in. Research in Veterinary Science, 91, 64-70.
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Gaag, L.C. van der & Bodlaender, H.L. (2011). On Stopping Evidence Gathering for Diagnostic Bayesian Networks. In W Liu (Ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 11th European Conference, ECSQARU 2011 Vol. 6717. Lecture Notes in Computer Science (pp. 170-181). Springer.
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Kwisthout, J., Bodlaender, H.L. & Gaag, L.C. van de. The Complexity of Finding the Most Probable Explanations in Probabilistic Networks. In I. Cerná, T. Gyimóthy, J. Hromkovic, K..G. Jeffery, R. Královic, M. Vukolic & S. Wolf (Eds.), SOFSEM 2011: Theory and Practice of Computer Science - 37th Conference on Current Trends in Theory and Practice of Computer Science Vol. 6543. Lecture Notes in Computer Science (pp. 356-367). Springer.
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Bertens, R., Renooij, S. & Gaag, L.C. van der (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). Gent.
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Woudenberg, S.P.D. & Gaag, L.C. van der (2011). Using the noisy-OR model can be harmful ... but it often is not. In W. Liu (Ed.), Proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011) Vol. 6717. Lecture Notes in Artificial Intelligence (pp. 122-133). Berlin: Springer.
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Pieters, B.F.I., Gaag, L.C. van der & Feelders, A.J. (2011). When Learning Naive Bayesian Classifiers Preserves Monotonicity. In Proceedings of ECSQARU 2011 (pp. 422-433). Springer.
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Bolt, J.H. & Gaag, L.C. van der (2010). An Empirical Study of the Use of the Noisy-Or Model in a Real-Life Bayesian Network. In E. Huellermeier, R. Kruse & F. Hoffmann (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 11-20). Springer.
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Rietbergen, M.T. & Gaag, L.C. van der (2010). Attaining Monotonicity for Bayesian Networks. In Proceedings of the 22nd Benelux Conference on Artificial Intelligence. Luxembourg.
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Steeneveld, W., Gaag, L.C. van der, Ouweltjes, W., Mollenhorst, H. & Hogeveen, H. (2010). Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. In Mastitis Research Into Practice, Proceedings of the 5th IDF Mastitis Conference (pp. 562-567).
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Steeneveld, W., Gaag, L.C. van der, Ouweltjes, W., Mollenhorst, H. & Hogeveen, H. (2010). Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. Journal of Dairy Science, 93, 2559-2568.
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Gaag, L.C. van der & Tabachneck-Schijf, H.J.M. (2010). Library-style ontologies to support varying model views. International Journal of Approximate Reasoning, 51, 196-208.
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Gaag, L.C. van der, Bolt, J.H., Loeffen, W.L.A. & Elbers, A.R.W. (2010). Modelling patterns of evidence in Bayesian networks: a case-study in Classical Swine Fever. In Computational Intelligence for Knowledge-based Systems Design, Vol. 6178. Lecture Notes in Artificial Intelligence (pp. 675-684). New York: Springer.
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Pieters, B.F.I. & Gaag, L.C. van der (2010). On Lurking Dependencies and Naive Bayesian Classifiers. In Proceedings of the 22nd Benelux Conference on Artificial Intelligence. Luxembourg.
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Gaag, L.C. van der, Renooij, S., Schijf, H.J.M., Elbers, A.R.W. & Loeffen, W.L.A. (2010). Probability Assessments from Multiple Experts: Qualitative Information is More Robust. In Proceedings of the 22nd Benelux Conference on Artificial Intelligence. Luxembourg.
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Steeneveld, W., Gaag, L.C. van der, Barkema, H.W. & Hogeveen, H. (2010). Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems. Computers and Electronics in Agriculture, 71, 50-56.
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Kwisthout, J.H.P., Bodlaender, H.L. & Gaag, L.C. van der (2010). The necessity of bounded treewidth for efficient inference in Bayesian networks. In H. Coelho, R. Studer & M. Wooldridge (Eds.), Proceedings of the 23rd European Conference on Artificial Intelligence, ECAI 2010 (pp. 237-242). Amsterdam: IOS Press.
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Gaag, L.C. van der, Renooij, S., Feelders, A.J., Groote, A.J. de, Eijkemans, M.J.C., Broekmans, F.J. & Fauser, B.C.J.M. (2009). Aligning Bayesian Network Classifiers with Medical Contexts. In P Perner (Ed.), Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition Vol. 5632. Lecture Notes in Computer Science (pp. 787-801). Berlin/Heidelberg: Springer-Verlag.
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Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2009). Constructing naive Bayesian classifiers for veterinary medicine: a case study in the clinical diagnosis of classical swine fever. onbekend: UU BETA ICS Departement Informatica.
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Gaag, L.C. van der & Schijf, H.J.M. (2009). Library-style ontologies to support varying model views. onbekend: UU BETA ICS Departement Informatica.
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Steeneveld, W., Gaag, L.C. van der, Barkema, H.W. & Hogeveen, H. (2009). Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks. Journal of Dairy Science, 92, 2598-2609.
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Gaag, L.C. van der, Renooij, S., Steeneveld, W. & Hogeveen, H. (2009). When in doubt ... be indecisive. In C Sossai & G Chemello (Eds.), Proceedings of the Tenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty Vol. 5590. Lecture Notes in Computer Science (pp. 518-529). Berlin Heidelberg: Springer Verlag.
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Gaag, L.C. van der, Renooij, S., Feelders, A.J., Groote, A.J. de, Eijkemans, M.J.C., Broekmans, F.J. & Fauser, B.C.J.M. (2008). Aligning Bayesian Network Classifiers With Medical Contexts. (UU-CS2008-015 ). onbekend: UU WINFI Informatica.
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Charitos, T., Waal, P.R. de & Gaag, L.C. van der (2008). Computing short interval transition matrices of a discrete-time Markov chain from partially observed data. Statistics in Medicine, 27(6), 905-921.
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Tabachneck-Schijf, H.J.M., Gaag, L.C. van der, Geenen, P.L., Schrage, M., Loeffen, W.L.A. & Elbers, A.R.W. (2008). Designing a personal digital assistant for early on-site detection of classical swine fever in a pig unit. In P. Evans (Ed.), Proceedings of the 20th International Pig Veterinary Science Congress. Durban, South Africa: Congress.
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Renooij, S. & Gaag, L.C. van der (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). Hirtshals.
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Renooij, S. & Gaag, L.C. van der (2008). Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial intelligence, 172(12-13), 1470-1494.
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Renooij, S. & Gaag, L.C. van der (2008). Evidence and Scenario Sensitivities in Naive Bayesian Classifiers. (UU-CS2008-040 ). onbekend: UU WINFI Informatica.
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Renooij, S. & Gaag, L.C. van der (2008). Evidence and scenario sensitivities in naive Bayesian classifiers. International Journal of Approximate Reasoning, 49(2), 398-416.
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Bolt, J.H. & Gaag, L.C. van der (2008). Loopy propagation: the posterior error at convergence nodes. In M..Poel A. Nijholt & G.H.W. Hondorp (Eds.), Prodeedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intellgence Conference (pp. 33-40).
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Kwisthout, J.H.P. & Gaag, L.C. van der (2008). The Computational Complexity of Sensitivity Analysis and Parameter Tuning. In D. McAllester & P. Myllymaki (Eds.), Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI'08) (pp. 349-356).
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Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2008). Validation of a clinical decision-support system for the early detection of classical swine fever. In P. Evans (Ed.), Proceedings of the 20th International Pig Veterinary Science. Durban, South Africa: Congress.
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Gaag, L.C. van der, Tabachneck-Schijf, H.J.M. & Geenen, P.L. (2008). Verifying Monotonicity of Bayesian Networks with Domain Experts. (UU-CS2008-014 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Charitos, T., Gaag, L.C. van der, Visscher, S., Schurink, C.A.M. & Lucas, P.J.F. (2007). A Dynamic Bayesian Network for Diagnosing Ventilator-Associated Pneumonia in ICU Patients. (UU-CS2007-013 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Charitos, T., Waal, P.R. de & Gaag, L.C. van der (2007). Convergence in Markovian models with implications for efficiency of inference. International Journal of Approximate Reasoning, 46(2), 300-319.
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Bolt, J.H. & Gaag, L.C. van der (2007). Decisiveness in loopy propagation. In P. Lucas, J.A. Gámez & A. Salmeron (Eds.), Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing, 214) (pp. 153-173). Berlin: Springer.
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Sent, D. & Gaag, L.C. van der (2007). Enhancing automated test selection in probabilistic networks. In R. Bellazzi, A. Abu-Hanna & J. Hunter (Eds.), Proceedings of Artifical Intelligence in Medicine Vol. 4594. Lecture Notes in Artificial Intelligence (pp. 331-335). Berlin Heidelberg: Springer-Verslag.
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Waal, P.R. de & Gaag, L.C. van der (2007). Inference and learning in multi-dimensional Bayesian network classifiers. In K. Mellouli (Ed.), European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty Vol. 4724. Lecture Notes in Computer Science (pp. 501-511). Berlin Heidelberg: Springer Verlag.
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Tabachneck-Schijf, H.J.M. & Gaag, L.C. van der (2007). Library-style ontologies to support varying model views. In K. Blackmond Laskey, J. Goldsmith & S.M. Mahoney (Eds.), Proceedings of the Fifth Bayesian Modeling Applications Workshop (pp. 78-87). Vancouver.
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Sent, D. & Gaag, L.C. van der (2007). On the behaviour of information measures for test selection. In R. Bellazzi, A. Abu-Hanna & J Hunter (Eds.), Proceedings of Artifical Intelligence in Medicine Vol. 4594. Lecture Notes in Artificial Intelligence (pp. 316-325). Berlin Heidelberg: Springer-Verslag.
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Helsper, E.M. & Gaag, L.C. van der (2007). Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain. In P. McBurney & S. Parsons (Eds.), Vol. 22. The knowledge engineering review (pp. 67-86).
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Gaag, L.C. van der, 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 (Studies in Fuzziness and Soft Computing, 213) (pp. 103-124). Berlin: Springer.
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Parr, R. & Gaag, L.C. van der (Eds.). (2007). Uncertainty in Artificial Intelligence. Proceedings of the Twenty-Third Conference. Corvallis: AUAI Press.
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Charitos, T., Visscher, S., Gaag, L.C. van der, Lucas, P.J.F. & Schurink, K. (2006). A dynamic model for therapy selection in ICU patients with VAP. In N. Peek & C. Combi (Eds.), Proceedings of the 11th Intelligent Data Analysis in bioMedicine and Pharmacology Workshop (pp. 71-76).
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Sent, D. & Gaag, L.C. van der (2006). Automated test selection in decision-support systems: a case study in oncology. In Ubiquity: Technologies for Better Health in Aging Societies - Proceedings of MIE2006 Vol. 124. Studies in Health Technology and Informatics (pp. 491-496). IOS Press.
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Charitos, T., Waal, P.R. de & Gaag, L.C. van der (2006). Convergence in Markovian Models with Implications for Efficiency of Inference. (UU-CS2006-038 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Bolt, J.H. & Gaag, L.C. van der (2006). Descisiveness in Loopy Propagation. (UU-CS2006-005 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Geenen, P.L., Elbers, A.R.W., Gaag, L.C. van der & Loeffen, W.L.A. (2006). Development of a probabilistic network for clinical detection of classical swine fever. In Proceedings of the 11th Symposium of the International Society for Veterinary Epidemiology and Economics (pp. 667-669). Cairns, Australia.
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Renooij, S. & Gaag, L.C. van der (2006). Enhanced Qualitative Probabilistic Networks for Resolving Trade-offs. (UU-CS2006-034 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Renooij, S. & Gaag, L.C. van der (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). Prague, Czech Republic.
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Gaag, L.C. van der, 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). Prague, Czech Republic.
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Feelders, A.J. & Gaag, L.C. van der (2006). Learning Bayesian network parameters under order constraints. International Journal of Approximate Reasoning, 42, 37-53.
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Gaag, L.C. van der & Waal, P.R. de (2006). Multi-dimensional Bayesian Network Classifiers. (CS-UU2006-056 ). onbekend: UU WINFI Informatica.
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Gaag, L.C. van der & Waal, P.R. de (2006). Multi-dimensional Bayesian Network Classifiers. In M Studeny & J Vomlel (Eds.), Proceedings of the Third European Workshop in Probabilistic Graphical Models (pp. 107-114). Prague.
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Gaag, L.C. van der & 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). Amsterdam, The Netherlands: Elsevier.
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Gaag, L.C. van der & Almond, R. (2006). Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition. Utrecht: Universiteit Utrecht.
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Charitos, T. & Gaag, L.C. van der (2006). Sensitivity analysis for threshold decision making with DBNs. In R. Dechter & T. Richardson (Eds.), Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (pp. 72-79). Corvallis: AUAI Press.
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Charitos, T. & Gaag, L.C. van der (2006). Sensitivity analysis of Markovian models. In G. Sutcliffe & R. Goebel (Eds.), Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (pp. 806-811). AAAI Press.
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Gaag, L.C. van der, Geenen, P.L. & Tabachneck-Schijf, H.J.M. (2006). Verifying monotonicity in Bayesian networks with domain experts. In L.C. van der Gaag & R. Almond (Eds.), Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition (pp. 9-15).
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Charitos, T., Gaag, L.C. van der, Visscher, S., Schurink, K. & Lucas, P.J.F. (2005). A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. In J.H. Holmes & N. Peek (Eds.), Proceedings of the 10th Intelligent Data Analysis in Medicine and Pharmacology Workshop (pp. 32-37). Aberdeen.
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Helsper, E.M., Gaag, L.C. van der, Feelders, A.J., Loeffen, W.L.A., Geenen, P.L. & Elbers, A.R.W. (2005). Bringing order into Bayesian-network construction. In Proceedings of the Third International Conference on Knowledge Capture (pp. 121-128). New York: ACM Press.
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Gaag, L.C. van der & Helsper, E.M. (2005). Defining classes of influences for the acquisition of probability constraints for Bayesian Networks. In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004).
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Geenen, P.L. & Gaag, L.C. van der (2005). Developing a Bayesian network for clinical diagnosis in veterinary medicine: from the individual to the herd. In Proceedings of the Third Bayesian Modelling Applications Workshop, held in conjunction with the Twenty-first Conference on Uncertainty in Artificial Intelligence. Edinburgh.
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Sent, D., Gaag, L.C. van der, Witteman, C.L.M., Aleman, B. M. P. & Taal, B.G. (2005). Eliciting test-selection strategies for a decision-support system in oncology. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour, 1(6), 543-561.
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Renooij, S. & Gaag, L.C. van der (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). Corvallis, OR: AUAI Press.
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Sent, D. & Gaag, L.C. van der (2005). Generalised reliability characteristics for probabilistic networks. Artificial Intelligence in Medicine, 34, 41-52.
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Helsper, E.M. & Gaag, L.C. van der (2005). Generic Knowledge Structures for Probabilistic-Network Engineering. (UU-CS2005-014 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Helsper, E.M. & Gaag, L.C. van der (2005). Generic knowledge structures for probabilistic-network engineering. In Proceedings of the Third Bayesian Modelling Applications Workshop, held in conjunction with the Twenty-first Conference on Uncertainty in Artificial Intelligence. Edinburgh.
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Schrage, M., IJzendoorn, A.F. & Gaag, L.C. van der (2005). Haskell ready to Dazzle the real world. In Proceedings of the 2005 ACM SIGPLAN Workshop on Haskell (pp. 17-26). New York: ACM Press.
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Bolt, J.H., Gaag, L.C. van der & Renooij, S. (2005). Introducing situational signs in qualitative probabilistic networks. International Journal of Approximate Reasoning, 38, 333-354.
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Feelders, A.J. & Gaag, L.C. van der (2005). Learning Bayesian network parameters with prior knowledge about context-specific qualitative influences. In F. Bacchus & T. Jaakkola (Eds.), Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, (pp. 193-200). Corvallis: AUAI Press.
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Feelders, A.J. & Gaag, L.C. van der (2005). Learning Bayesian Network Parameters Under Order Constraints. (UU-CS2005-58 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2005). Naive Bayesian classifiers for the clinical diagnosis of Classical Swine Fever. In D.J. Mellor, A.M. Russell & J.L.N. Wood (Eds.), Proceedings of the Meeting of the Society for Veterinary Epidemiology and Preventive Medicine (pp. 169-176). Nairn,Schotland.
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Charitos, T., Waal, P.R. de & Gaag, L.C. van der (2005). Speeding up inference in Markovian models. In I. Russell & Z. Markov (Eds.), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (pp. 785-790).
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Waal, P.R. de & Gaag, L.C. van der (2005). Stable Independence and Complexity of Representation. (CS-UU2005-56 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Waal, P.R. de & Gaag, L.C. van der (2005). Stable Independence and Complexity of Representation. (UU-CS2005-056 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Waal, P.R. de & Gaag, L.C. van der (2005). Stable Independence in Perfect Maps. onbekend: UU WINFI Informatica.
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Waal, P.R. de & Gaag, L.C. van der (2005). Stable independence in perfect maps. In F. Bacchus & T. Jaakkola (Eds.), Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence (pp. 161-168). Corvallis: AUAI Press.
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Drugan, M.M. & Gaag, L.C. van der (2004). A New MDL-based Function for Feature Selection for Bayesian Network Classifiers. In R. López de Mántaras & L. Saitta (Eds.), Proceedings of the 16th European Conference on Artificial Intelligence (pp. 999-1000). Amsterdam: IOS Press.
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Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2004). Building naive Bayesian classifiers from literature: a case study in classical swine fever. In R. Verbrugge, N. Taagten & L. Schomaker (Eds.), Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 227-234). Groningen: University of Groningen.
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Bolt, J.H. & Gaag, L.C. van der (2004). Decisiveness in loopy propagation. In P. Lucas (Ed.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (pp. 25-32).
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Gaag, L.C. van der & Helsper, E.M. (2004). Defining classes of influences for the acquisition of probability constraints for Bayesian Networks. In R. López de Mántaras & L. Saitta (Eds.), Proceedings of the 16th European Conference on Artificial Intelligence (pp. 1101-1102). Amsterdam: IOS Press.
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Helsper, E.M., Gaag, L.C. van der & Groenendaal, F. (2004). Designing a procedure for the acquisition of probability constraints for Bayesian networks. In Motta E. et al (Ed.), Engineering Knowledge in the Age of the Semantic Web (pp. 280-292). Berlin: Springer-Verlag Berlin Heidelberg 2004.
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Lucas, P.J.F., Gaag, L.C. van der & Abu-Hanne, A. (2004). Editorial: Bayesian models in biomedicine and health-care. Artificial Intelligence in Medicine, 30(3), 201-214.
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Gaag, L.C. van der & 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). Arlington: AUAI Press.
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Bolt, J.H., Gaag, L.C. van der & Renooij, S. (2004). Introducing Situational Signs in Qualitative Probabilistic Networks. (UU-CS2004-006 ). Utrecht: Utrecht University: Information and Computing Sciences.
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Feelders, A.J. & Gaag, L.C. van der (2004). Learning Bayesian Network Parameters Under Order Constraints. In P. Lucas (Ed.), Proceedings of the second European workshop on probabilistic graphical models (PGM'04) (pp. 73-80).
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Gaag, L.C. van der, Bodlaender, H.L. & Feelders, A.J. (2004). Monotonicity in Bayesian Networks. In M. Chickering & J. Halpern (Eds.), Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (pp. 569-576). AUAI Press.
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Bolt, J.H. & Gaag, L.C. van der (2004). On the convergence error in loopy propagation. In R. Verbrugge, N. Taatgen & L. Schomaker (Eds.), Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 267-274).
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Geenen, P.L., Gaag, L.C. van der, Loeffen, W.L.A. & Elbers, A.R.W. (2004). On the robustness of feature selection with absent and non-observed features. In J.M. Barreiro, F. Martin-Sanchez, V. Maojo & F. Sanz (Eds.), Proceedings of the Fifth International Symposium on Biological and Medical Data Analysis (pp. 148-159). Heidelberg: Springer-Verlag.
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Gaag, L.C. van der & Renooij, S. (2004). On the sensitivity of probabilistic networks to test reliability. In Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1675-1682).
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Blanco, R., Gaag, L.C. van der, Inza, I. & Larranaga, P. (2004). Selective classifiers can be too restrictive: a case-study in oesophageal cancer. In Proceedings of the Fifth International Symposium on Biological and Medical Data Analysis (pp. 212-223). Heidelberg: Springer-Verlag.
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Charitos, T. & Gaag, L.C. van der (2004). Sensitivity properties of Markovian models. In Advances in Intelligent Systems - Theory and Applications.. IEEE Computer Society.
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Waal, P.R. de & Gaag, L.C. van der (2004). Stable Independence and Complexity of Representation. In M. Chickering & J. Halpern (Eds.), Proceedings of the Twentieth Conference on Uncertainty in Artifical Intelligence. (pp. 112-119). Arlington: UAI Press.
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Bolt, J.H. & Gaag, L.C. van der (2004). The convergence error in loopy propagation. In International Conference on Advances in Intelligent Systems - Theory and Applications. IEEE Computer Society.
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Gaag, L.C. van der, Bolt, J.H. & Renooij, S. (2004). The practicability of situational signs for QPNs. In Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 1691-1698). Perugia,Italy.
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Gaag, L.C. van der (2003). Model-based reasoning with qualitative probabilistic networks. In P. Lucas (Ed.), Model-based Reasoning and Qualitative Reasoning in Biomedicine, Working Notes of the Workshop held at the 9th European Conference on Artificial Intelligence in Medicine, Invited Talks (pp. 9-15).
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Dijk, S.F. van, Gaag, L.C. van der & Thierens, D. (2003). A Skeleton-Based Approach to Learning Bayesian Networks from Data. In Lecture Notes in Computer Science, Volume 2838: Proceedings of the Seventh Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 132-143). Springer.
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Dijk, S.F. van, Thierens, D. & Gaag, L.C. van der (2003). Building a GA from Design Principles for Learning Bayesian Networks. In Erick Cantú-Paz, James.A. Foster, Kalyanmoy Deb, Lawrence Davis, Rajkumar Roy, Una-May O'Reilly, Hans-Georg Beyer, Russell.K. Standish, Graham Kendall, Stewart.W. Wilson, Mark Harman, Joachim Wegener, Dipankar Dasgupta, Mitchell.A Potter, Alan.C. Schultz, Kathryn.A. Dowsland, Natasa Jonoska & Julian.F. Miller (Eds.), Lecture Notes in Computer Science, Volume 2723: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 886-897). Springer-Verlag.
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Rijsinge, W.P. van, Gaag, L.C. van der, Visseren, F.L.J. & Graaf, Y van der (2003). Compliance with the hyperlipidaemia consensus: clinicians versus the computer. In M. Dojat, E. Keravnou & P. Barahona (Eds.), Artificial Intelligence in Medicine, Lecture Notes in Artificial Intelligence 2780 (pp. 340-344). Berlin: Springer Verlag.
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Sent, D. & Gaag, L.C. van der (2003). Detailing Test Characteristics for Probabilistic Networks. In M. Dojat, E.T. Keravnou & P. Barahona (Eds.), Proceedings of the 9th conference on Artificial Intelligence in Medicine in Europe, Lecture Notes in Artificial Intelligence 2780 (pp. 254-263). Berlijn: Springer.
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Bolt, J.H., Gaag, L.C. van der & 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, Lecture Notes in Artificial Intelligence (pp. 113-124). Berlin: Springer-Verlag.
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Sent, D., Gaag, L.C. van der, Witteman, C.L.M., Aleman, B. M. P. & Taal, B.G. (2003). On the Use of Vignettes for Eliciting Test-selection Strategies. In R. Baud, M. Fieschi, P. Le Beux & P. Ruch (Eds.), The New Navigators: from Professionals to Patients; Proceedings of MIE2003. Amsterdam: IOS Press.
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Helsper, E.M. & Gaag, L.C. van der (2003). Ontologies for Probabilistic Networks. onbekend: UU WINFI Informatica en Informatiekunde.
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Helsper, E.M. & Gaag, L.C. van der (2003). Ontologies for Probabilistic Networks. (UU-CS2003-042 ). onbekend: UU WINFI Informatica en Informatiekunde.
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Gaag, L.C. van der & 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). Berlin: Springer Verlag.
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Sent, D., Gaag, L.C. van der, Witteman, C.L.M., Aleman, B. M. P. & Taal, B.G. (2003). The elicitation of test-selection strategies: a case study in oncology. (UU-CS2003-053 ). Utrecht, Netherlands: Utrecht University: Information and Computing Sciences.
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Bolt, J.H., Renooij, S. & Gaag, L.C. van der (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). San Francisco, California: Morgan Kaufmann Publishers.
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Helsper, E.M. & Gaag, L.C. van der (2002). A case study in ontologies for probabilistic networks. In M. Bramer, F. Coenen & A. Preece (Eds.), Research and Development in Intelligent Systems XVIII (pp. 229-242). London, England: Springer-Verlag.
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Helsper, E.M. & Gaag, L.C. van der (2002). Building Bayesian networks through ontologies. In F. van Harmelen (Ed.), Proceedings of the 15th European Conference on Artificial Intelligence (pp. 680-684). Amsterdam, the Netherlands: IOS Press.
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Helsper, E.M. & Gaag, L.C. van der (2002). Building Bayesian networks through ontologies. In H. Blockeel & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 423-424). Leuven, Belgium.
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Renooij, S., Gaag, L.C. van der & Parsons, S. (2002). Context-specific Sign-propagation in Qualitative Probabilistic Networks. (UU-CS2002-024 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S., Gaag, L.C. van der & Parsons, S.D. (2002). Context-specific sign-propagation in qualitative probabilistic networks. Artificial intelligence, 140, 207-230.
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Gaag, L.C. van der & Helsper, E.M. (2002). Experiences with modelling issues in building probabilistic networks. In A. Gómez-Pérez & V.R. Benjamins (Eds.), Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web, Proceedings of EKAW 2002 (pp. 21-26). Berlin, Germany: Springer-Verlag.
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Renooij, S. & Gaag, L.C. van der (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). San Francisco, USA: Morgan Kaufman Publishers.
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Drugan, M.M., Thierens, D. & Gaag, L.C. van der (2002). MDL-based feature selection for Bayesian network classifiers. In M. Blockeel & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 99-106). Leuven, Belgium.
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Bodlaender, H.L., Van den Eijkhof, F. & Gaag, L.C. van der (2002). On the complexity of the MPA problem in probabilistic networks. In F. van Harmelen (Ed.), Proceedings 15th European Conference on Artificial Intelligence (pp. 675-679). Amsterdam, the Netherlands: IOS Press.
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Gaag, L.C. van der, Renooij, S., Witteman, C.L.M., Aleman, B. M. P. & Taal, B.G. (2002). Probabilities for a probabilistic network: A case-study in oesophageal cancer. Artificial Intelligence in Medicine, 25(2), 123-148.
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Renooij, S., Gaag, L.C. van der & Parsons, S.D. (2002). Propagation of multiple observations in QPNs revisited. In F. van Harmelen (Ed.), Proceedings of the Fifteenth European Conference on Artificial Intelligence (pp. 665-669). Amsterdam, the Netherlands: IOS Press.
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Coupé, V.M.H. & Gaag, L.C. van der (2002). Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36, 323-356.
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Sent, D. & Gaag, L.C. van der (2002). Test selection: The Gini index and the Shannon entropy behave differently. In M. Blockeel & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 291-298). Leuven, Belgium.
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Gaag, L.C. van der & 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). Amsterdam, The Netherlands: Universiteit van Amsterdam.
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Gaag, L.C. van der & Renooij, S. (2001). Analysing sensitivity data. In J. Breese & D. Koller (Eds.), Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (pp. 530-537). San Francisco, U.S.A.: Morgan Kaufmann Publishers.
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Lucas, P.J.F., Gaag, L.C. van der & Abu-Hanna, A. (2001). Bayesian Models in Medicine. Cascais, Portugal: Utrecht University.
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Renooij, S., Parsons, S. & Gaag, L.C. van der (2001). Context-specific sign-propagation in qualitative probabilistic networks. In B. Nebel (Ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 667-672). San Francisco, California, U.S.A.: Morgan Kaufmann Publishers.
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Sent, D., Gaag, L.C. van der & Witteman, C.L.M. (2001). Modelling test characteristics in 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. 433-440). Amsterdam, The Netherlands: Universiteit van Amsterdam.
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Bodlaender, H.L., Van den Eijkhof, F. & Gaag, L.C. van der (2001). On the MPA problem in 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. 59-66). Amsterdam: Universiteit van Amsterdam.
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Gaag, L.C. van der & Renooij, S. (2001). On the evaluation of probabilistic networks. In S. Quaglini, P. Barahona & S. Andreassen (Eds.), Artificial Intelligence in Medicine (pp. 457-461). Berlin, Germany: Springer-Verlag.
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Helsper, E.M. & Gaag, L.C. van der (2001). Ontologies for probabilistic networks: A case study in oesophageal cancer. In B. Kröse, M. de Rijke, G. Schreiber & M. van Someren (Eds.), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (pp. 125-132). Amsterdam: Universiteit van Amsterdam.
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Bodlaender, H.L., Koster, A.M.C., Van den Eijkhof, F. & Gaag, L.C. van der (2001). Pre-processing for triangulation of 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. 67-68). Amsterdam: Universiteit van Amsterdam.
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Bodlaender, H.L., Koster, A.M.C., Van den Eijkhof, F. & Gaag, L.C. van der (2001). Pre-processing for triangulation of probabilistic networks. In J. Breese & D. Koller (Eds.), Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (pp. 32-39). San Francisco: Morgan Kaufmann.
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Gaag, L.C. van der, Renooij, S., Witteman, C.L.M., Aleman, B. M. P. & Taal, B.G. (2001). Probabilities for a probabilistic network: A case-study in Oesophageal Carcinoma. (UU-CS2001-01 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der, 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.), Artificial Intelligence in Medicine (pp. 188-198). Berlin: Springer-Verlag.
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Druzdzel, M.J. & Gaag, L.C. van der (2000). Building probabilistic networks: "Where do the numbers come from ?". IEEE Transactions on Knowledge and Data Engineering, 12, 481-486.
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Druzdzel, M.J. & Gaag, L.C. van der (2000). Building probabilistic networks: Where do the numbers come from? - a guide to the literature. (UU-CS2000-20 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der, Renooij, S., Aleman, B. M. P. & Taal, B.G. (2000). Evaluation of a probabilistic model for staging of oesophageal carcinoma. (UU-CS2000-16 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der, 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). Amsterdam: IOS Press.
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Renooij, S. & Gaag, L.C. van der (2000). Exploiting non-monotonic influences in qualitative belief networks. (UU-CS2000-17 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S. & Gaag, L.C. van der (2000). Exploiting non-monotonic influences in qualitative belief networks. In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (pp. 1285-1290). Madrid, Spain.
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Kjaerulff, U. & Gaag, L.C. van der (2000). Making sensitivity analysis computationally efficient. In C. Boutilier & M. Goldszmidt (Eds.), Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (pp. 317-325). San Francisco: Morgan Kaufmann Publishers.
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Renooij, S., Gaag, L.C. van der, Parsons, S. & Green, L.A. (2000). Pivotal pruning of trade-offs in QPNs. (UU-CS2000-18 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S., Gaag, L.C. van der, 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). San Francisco, California: Morgan Kaufmann Publishers.
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Renooij, S., Gaag, L.C. van der & Parsons, S. (2000). Propagation of multiple observations in qualitative probabilistic networks. In A. van den Bosch & H. Weigand (Eds.), Proceedings of the Twelfth Belgium-Netherlands Artificial Intelligence Conference (pp. 235-242). Tilburg: Tilburg University.
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Gaag, L.C. van der & Coupé, V.M.H. (2000). Sensitivity analysis for threshold decision making with Bayesian belief networks. In E. Lamma & P. Mello (Eds.), AI*IA 99: Advances in Artificial Intelligence (pp. 37-48). Berlin: Springer-Verlag.
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Coupé, V.M.H., Gaag, L.C. van der & Habbema, J.D.F. (2000). Sensitivity analysis: an aid for probability elicitation. The knowledge engineering review, 15, 215-232.
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Renooij, S., Gaag, L.C. van der, 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). Menlo Park, California: AAAI Press.
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Renooij, S. & Gaag, L.C. van der (1999). Enhancing QPNs for trade-off resolution. (UU-CS1999-23 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S. & Gaag, L.C. van der (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). San Francisco, CA: Morgan Kaufmann Publishers.
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Renooij, S. & Gaag, L.C. van der (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).
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Gaag, L.C. van der, Renooij, S., Witteman, C.L.M., Aleman, B. & Taal, B. (1999). How to elicit many probabilities. In K.B. Caskey & H. Prade (Eds.), Uncertainty in artificial intelligence (pp. 647-654). San Francisco, CA: Kaufman.
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Gaag, L.C. van der, 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 (pp. 647-654). San Fransisco, CA: Morgan Kaufmann Publishers.
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Gaag, L.C. van der, Renooij, S., Witteman, C.L.M. & Aleman, B. M. P. (1999). How to elicit many probabilities. (UU-CS1999-15 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der (1999). Properties of sensitivity analysis of Bayesian belief networks. (UU-CS1999-29 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der & Coupe, V.M.H. (1999). Sensitivity analysis for threshold decision making with Bayesian belief networks. (UU-CS1999-32 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der & Coupé, V.M.H. (1999). Sensitivity analysis for threshold decision making with Bayesian belief networks. In E. Lamma & P. Mello (Eds.), Proceedings of the Sixth Conference of the Italian Association for Artificial Intelligence (pp. 453-462). Bologna, Italy: Pitagora Editrice.
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Coupe, V.M.H., Gaag, L.C. van der & Habbema, J.D.F. (1999). Sensitivity analysis: an aid for belief-network quantification. (UU-CS1999-13 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S. & Gaag, L.C. van der (1998). Decision making in qualitative influence diagrams. (UU-CS1998-03 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S. & Gaag, L.C. van der (1998). Decision making in qualitative influence diagrams. In D.J. Cook (Ed.), Proceedings of the Eleventh International FLAIRS Conference (pp. 410-414). Menlo Park, California, U.S.A..
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Coupé, V.M.H. & Gaag, L.C. van der (1998). Exploiting properties of sensitivity analysis for belief betworks. In Proceedings of the Second International Symposium on Sensitivity Analysis of Model Output, Joint Research Centre of the European Commission (pp. 75-78).
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Gaag, L.C. van der & Meyer, J-J.Ch. (1998). Informational independence: models and normal forms. International journal of intelligent systems, 13, 83-109.
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Coupe, V.M.H. & Gaag, L.C. van der (1998). Practicable sensitivity analysis of Bayesian belief networks. (UU-CS1998-10 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Coupé, V.M.H. & Gaag, L.C. van der (1998). Practicable sensitivity analysis of bayesian belief networks. In M. Huskova, P. Lachout & J.A. Visek (Eds.), Proceedings of the Joint Session of the 6th Praque Symposium of Asymptotic Statistics and the 13th Praque Conference on Information Theory, Statistical Decision Functions and Random Processes (pp. 81-86). Praque: Union of Czech Mathematicians and Physicists.
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Wel, F.J.M. van der, Gaag, L.C. van der & Gorte, B.G.H. (1998). Visual exploration of uncertainty in remote sensing classifications. Computers and Geo Sciences, 24, 335-343.
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Gaag, L.C. van der & Bodlaender, H.L. (1997). Comparing loop cutsets and clique trees in probabilistic inference. In K. Van Marcke & W. Daelemans (Eds.), Proceedings of the 9th Dutch Conference on Artificial Intelligence (pp. 71-80). Nederlandse Vereniging voor Kunstmatige Intelligentie (NVKI).
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Gaag, L.C. van der & Bodlaender, H.L. (1997). Comparing loop cutsets and clique trees in probabilistic inference. (UU-CS1997-42 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Renooij, S. & Gaag, L.C. van der (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). Antwerp, Belgium: University of Antwerp.
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Gaag, L.C. van der & Meyer, J-J.Ch. (1997). Informational independence: Models and normal forms. (UU-CS1997-17 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Coupé, V.M.H. & Gaag, L.C. van der (1996). Supporting probability elicitation by sensitivity analysis. In E. Plaza & R. Benjamins (Eds.), Tenth European Workshop on Knowledge Acquisition, Modeling and Management (pp. 335-340). Berlin, Germany: Springer-Verlag.
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Wel, F.J.M. van der & Gaag, L.C. van der (1997). Visual exploration of uncertainty in remote-sensing classification. (UU-CS1997-29 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der (1996). Bayesian belief networks - a guest editor's introduction. AISB quarterly, 94(8).
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Gaag, L.C. van der (1996). Bayesian belief networks: Odds and ends. (UU-CS1996-14 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der (1996). Bayesian belief networks: odds and ends. The Computer Journal, 39, 97-113.
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Gaag, L.C. van der & Meyer, J-J.Ch. (1996). Characterising normal forms for informational independence. In Proceedings of the Sixth International Conference On Information Pocessing and management of Uncertainty in Knowledge-Based Systems (pp. 973-978).
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Gaag, L.C. van der & Meyer, J-J.Ch. (1996). Characterizing normal forms for informational independence. (UU-CS1996-21 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gorte, B.G.H. & Gaag, L.C. van der (1996). Decision-analytic interpretation of remotely sensed data. (UU-CS1996-31 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gorte, B.G.H., Gaag, L.C. van der & Wel, F. van der (1996). Decision-analytic interpretation of remotely sensed data. In M.J. Kraak & M. Molenaar (Eds.), Proceedings of the 7th International Symposium on Spatial Data Handling (pp. 11B.31-11B.42).
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Gaag, L.C. van der (1996). On evidence absorption for belief networks. (UU-CS1996-28 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der (1996). On evidence absorption for belief networks. International Journal of Approximate Reasoning, 15, 265-286.
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Meyer, J-J.Ch. & Gaag, L.C. van der (1996). Proceedings of the 8th Dutch Conference on Artificial Intelligence. Utrecht, the Netherlands: Utrecht University.
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Gaag, L.C. van der & Meyer, J-J.Ch. (1996). The dynamics of probabilistic structural relevance. (UU-CS1996-47 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Gaag, L.C. van der & Meyer, J-J.Ch. (1996). The dynamics of probabilistic structural relevance. In J-J.Ch. Meyer & L.C. van der Gaag (Eds.), Proceedings of the Eighth Dutch Conference on Artificial Intelligence (pp. 145-156). Utrecht, the Netherlands: Utrecht University.
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Jaspers, C.A.J.J. & Gaag, L.C. van der (1996). Utiliteitsmeting ten behoeve van Geautomatiseerde Behandelingskeuze voor Oesofagus Carcinoom. (UU-CS1996-48 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Jaspers, M.W.M., Gaag, L.C. van der, Derksen, A., Taal, B.G. & Aleman, B. M. P. (1996). Utiliteitsmeting ten behoeve van geautomatiseerde behandelingskeuze voor oesofagus carcinoom. In C. Stevens & G. de Moor (Eds.), Proceedings Medische Informatica (pp. 103-112).
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Peek, N.B. & Gaag, L.C. van der (1995). A Case-Based Filter for Diagnostic Belief Networks. (UU-CS1995-11 ). Utrecht: Utrecht University.
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Druzdzel, M.J. & Gaag, L.C. van der (1995). Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information. (UU-CS1995-23 ). Utrecht: Utrecht University.
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Gaag, L.C. van der (1994). Efficient multiple-disorder diagnosis by strategy focusing. (UU-CS1994-23 ). Utrecht.
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Gaag, L.C. van der (1994). Evidence absorption -- Experiments on different classes of randomly generated belief networks. (UU-CS1994-42 ). Utrecht.
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Gaag, L.C. van der (1994). Reason Maintenance for Production Systems. (UU-CS1994-13 ). Utrecht.
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Bruza, P.D. & Gaag, L.C. van der (1993). Efficient context-sensitive plausible inference for information disclosure. (RUU-CS93-42 ). Utrecht.
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Gaag, L.C. van der (1993). Evidence Absorption for Belief Networks. (RUU-CS93-35 ). Utrecht.
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Gaag, L.C. van der (1993). Selective evidence gathering for diagnostic belief networks. (RUU-CS93-31 ). Utrecht.
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Bruza, P.D. & Gaag, L.C. van der (1992). Index expression belief networks for information disclosure. (RUU-CS92-21 ). Utrecht.
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Gaag, L.C. van der (1992). Pearl's belief propagation; the proofs. (RUU-CS92-47 ). Utrecht.
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Kelleher, M. & Gaag, L.C. van der (1992). The LazyRMS: Avoiding work in the ATMS. (RUU-CS92-20 ). Utrecht.
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Gaag, L.C. van der (1990). A pragmatical view on the certainty factor model. (RUU-CS90-42 ). Utrecht.
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Gaag, L.C. van der (1990). On probability intervals and their updating. (RUU-CS90-22 ). Utrecht.
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