prof. dr. ir. L.C. (Linda) van der Gaag
L.C.vanderGaag@uu.nl
Gegenereerd op 2018-07-23 19:47:13


Chair
Decision-support Systems
Date of appointment 01.05.2000
Profile


Strategic themes / Focus areas
Gegenereerd op 2018-07-23 19:47:13
Curriculum vitae


Gegenereerd op 2018-07-23 19:47:13


All publications
  2018 - Scholarly publications
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 - Scholarly publications
Bolt, J.H. & van der Gaag, L.C. (2017). Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers. International Journal of Approximate Reasoning, 80, (pp. 361-376) (16 p.).
Lopatatzidis, S. & van der Gaag, L.C. (2017). Concise representations and construction algorithms for semi-graphoid independency models. International Journal of Approximate Reasoning, (pp. 377-392).
van der Gaag, L.C. & Lopatatzidis, S. (2017). Exploiting stability for compact representation of independency models. In A. Antonucci, L. Cholvy & O. Papini (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings (pp. 104-114). Berlin: Springer.
  2016 - Scholarly publications
Masegosa, Andrés, Feelders, A.J. & van der Gaag, L.C. (2016). Learning from incomplete data in Bayesian networks with qualitative influences. International Journal of Approximate Reasoning, 69 (C), (pp. 18-34 ) (17 p.).
  2015 - Scholarly publications
Woudenberg, Steven, van der Gaag, Linda & Rademaker, C.M.A. (05.06.2015). An intercausal cancellation model for Bayesian-network engineering. International Journal of Approximate Reasoning, 63, (pp. 32-47) (16 p.).
Bolt, J.H. & van der Gaag, L.C. (2015). Balanced tuning of multi-dimensional Bayesian network classifiers. In S. Destercke & Th. Denoeux (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings (pp. 210-220). Springer.
Lopatatatzidis, S. & van der Gaag, L.C. (2015). Computing concise representations of semi-graphoid independency models. In S. Destercke & Th. Denoeux (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings (pp. 290-300). Berlin: Springer.
Pastink, A.J. & van der Gaag, L.C. (2015). Multi-classifiers of Small Treewidth. In Sebastien Destercke & Thierry Denoeux (Eds.), ECSQARU 2015: 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 199-209) (11 p.). Springer.
Woudenberg, Steven & van der Gaag, Linda (2015). Propagation effects of model-calculated probability values in Bayesian networks. International Journal of Approximate Reasoning, 61, (pp. 1-15).
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.
  2014 - Scholarly publications
Krak, Thomas & van der Gaag, Linda (2014). Knowledge-based bias correction- A case study in veterinary decision support. In T. Schaub, G. Friedrich & B. O'Sullivan (Eds.), ECAI 2014 : 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic (pp. 489-494) (6 p.). Amsterdam: IOS Press.
Rietbergen, Merel, van der Gaag, Linda & Bodlaender, Hans (2014). Provisional Propagation for Verifying Monotonicity of Bayesian Networks. In Torsten Schaub, Gerhard Friedrich & Barry O. Sullivan (Eds.), ECAI 2014 - 21st European Conference on Artificial Intelligence (pp. 759-764). IOS Press.
Woudenberg, Steven, van der Gaag, Linda, Feelders, Adrianus & Elbers, Armin (2014). Real-time adaptive problem detection in poultry. Ecai 2014 - 21st european conference on artificial intelligence, 18-22 August 2014, Prague, Czech Republic (pp. 1217-1218) (2 p.). Amsterdam.
Woudenberg, Steven, van der Gaag, Linda, Feelders, Adrianus & Elbers, Armin (2014). Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability. Advances in Intelligent Data Analysis XIII Springer.
Pastink, Arnoud & van der Gaag, Linda (2014). The Persistence of Most Probable Explanations in Bayesian Networks. In T. Schaub, G. Friedrich & B. O'Sullivan (Eds.), ECAI 2014 - 21st European Conference on Artificial Intelligence, 18–22 August 2014, Prague, Czech Republic – Including Prestigious Applications of Intelligent Systems (PAIS 2014) (pp. 693-698) (6 p.).
  2013 - Scholarly publications
Woudenberg, Steven, van der Gaag, Linda & Elbers, Armin (05.02.2013). Development of a clinical decision-support system for early detection of low pathogenic avian influenza outbreaks in poultry. Proceedings of the 3rd Annual Meeting of AniBioThreat; Are, Sweden (pp. 65).
van der Gaag, L.C., Kuijper, R., Geffen, Y.M. & Vermeulen, J.L. (2013). Towards uncertainty analysis of Bayesian networks. In K. Hindriks, M. de Weerdt, B. van Riemsdijk & M. Warnier (Eds.), Proceedings of the 25th Benelux Conference on Artificial Intelligence (pp. 223-230) (8 p.).
  2013 - Professional publications
van der Gaag, L.C. (2013). Challenging Doaitse by Bayesianisms. In A. Dijkstsra & J. Hage (Eds.), Liber Amicorum for Doaitse Swierstra (pp. 258-263) (6 p.). Utrecht: Departement Informatica, Universiteit Utrecht.
  2012 - Scholarly publications
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.
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.
Woudenberg, S.P.D. & van der Gaag, L.C. (2012). Intercausal Cancellation in Bayesian Networks. In J.W.H.M. Uiterwijk (Eds.), Proceedings of the 24th Benelux Conference on Artificial Intelligence (pp. 242-249) (8 p.).
van der Gaag, Linda, Bodlaender, Hans L. & Feelders, Ad (2012). Monotonicity in Bayesian Networks. CoRR, abs/1207.4160.
Bolt, J.H. & van der Gaag, L.C. (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) (8 p.).
van der Gaag, L.C., 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) (8 p.).
  2011 - Scholarly publications
Rietbergen, M.T. & van der Gaag, L.C. (29.06.2011). Attaining Monotonicity for Bayesian Networks. In W Liu (Eds.), Proceedings 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011) (pp. 134-145) (12 p.). Belfast: Springer, European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty.
Geenen, P.L., van der Gaag, L.C., 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, (pp. 64-70) (7 p.).
van der Gaag, L.C. & Bodlaender, H.L. (2011). On Stopping Evidence Gathering for Diagnostic Bayesian Networks. In W Liu (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 11th European Conference, ECSQARU 2011 (pp. 170-181) (12 p.). Springer, Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 11th European Conference, ECSQARU 2011.
Kwisthout, J., Bodlaender, H.L. & van der Gaag, L.C. (2011). 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 (pp. 356-367) (12 p.). Springer, SOFSEM 2011: Theory and Practice of Computer Science - 37th Conference on Current Trends in Theory and Practice of Computer Science.
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.
Woudenberg, S.P.D. & van der Gaag, L.C. (2011). Using the noisy-OR model can be harmful ... but it often is not. In W. Liu (Eds.), Proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011) (pp. 122-133) (12 p.). Berlin: Springer.
Pieters, B.F.I., van der Gaag, L.C. & Feelders, A.J. (2011). When Learning Naive Bayesian Classifiers Preserves Monotonicity. Proceedings of ECSQARU 2011 (pp. 422-433) (12 p.). Springer.
  2010 - Scholarly publications
Bolt, J.H. & van der Gaag, L.C. (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) (10 p.). Springer.
Rietbergen, M.T. & van der Gaag, L.C. (25.10.2010). Attaining Monotonicity for Bayesian Networks. Proceedings of the 22nd Benelux Conference on Artificial Intelligence Luxembourg, 22nd Benelux Conference on Artificial Intelligence.
Steeneveld, W., van der Gaag, L.C., Ouweltjes, W., Mollenhorst, H. & Hogeveen, H. (2010). Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. Mastitis Research Into Practice, Proceedings of the 5th IDF Mastitis Conference (pp. 562-567) (6 p.).
Steeneveld, W., van der Gaag, L.C., 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, (pp. 2559-2568) (10 p.).
van der Gaag, L.C. & Tabachneck-Schijf, H.J.M. (2010). Library-style ontologies to support varying model views. International Journal of Approximate Reasoning, 51, (pp. 196-208) (13 p.).
van der Gaag, L.C., 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. Computational Intelligence for Knowledge-based Systems Design, (pp. 675-684) (10 p.). New York: Springer.
Pieters, B.F.I. & van der Gaag, L.C. (25.10.2010). On Lurking Dependencies and Naive Bayesian Classifiers. Proceedings of the 22nd Benelux Conference on Artificial Intelligence Luxembourg, 22nd Benelux Conference on Artificial Intelligence.
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.
Steeneveld, W., van der Gaag, L.C., 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, (pp. 50-56) (7 p.).
Kwisthout, J.H.P., Bodlaender, H.L. & van der Gaag, L.C. (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) (6 p.). Amsterdam: IOS Press, European Conference on Artificial Intelligence.
  2009 - Scholarly publications
Steeneveld, Wilma, van der Gaag, Linda, Barkema, H.W. & Hogeveen, H. (2009). A cow-specific probability of having clinical mastitis for use in automatic milking systems. In C. Lokhorst & P.W.G. Groot Koerkamp (Eds.), Precision livestock farming '09 (pp. 323-330). Wageningen: Wageningen Academic Publishers.
Charitos, T., van der Gaag, Linda, Visscher, S., Schurink, C.A.M. & Lucas, P.J.F. (2009). A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Systems with Applications, 36, (pp. 1249-1258).
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.
Steeneveld, Wilma, van der Gaag, Linda, Barkema, H.W. & Hogeveen, H. (2009). Bayesian networks for mastitis management on dairy farms. Proceedings of the 2009 Meeting of the Society for Veterinary Epidemiology and Preventive Medicine (pp. 126-135). London.
Steeneveld, W., van der Gaag, L.C., 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, (pp. 2598-2609) (12 p.).
Steeneveld, Wilma, van der Gaag, Linda, Barkema, H.W. & Hogeveen, H. (14.08.2009). Providing probability distributions for the Gram-status of clinical mastitis cases in dairy cattle. In A. Bregt, S. Wolfert, J.E. Wien & C. Lokhorst (Eds.), EFITA conference ’09. Proceedings of the 7th EFITA Conference, Wageningen, The Netherlands, 6-8 July 2009 (pp. 49-56). Wageningen: Wageningen Academic Publishers.
van der Gaag, Linda, (Tabachneck-)Schijf, H.J.M. & Geenen, P.L. (2009). Verifying monotonicity of Bayesian networks with domain experts. International Journal of Approximate Reasoning, 50, (pp. 429-436).
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.
  2008 - Scholarly publications
Charitos, T., de Waal, P.R. & van der Gaag, L.C. (2008). Computing short interval transition matrices of a discrete-time Markov chain from partially observed data. Statistics in Medicine, 27 (6), (pp. 905-921) (17 p.).
Tabachneck-Schijf, H.J.M., van der Gaag, L.C., 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 (Eds.), Proceedings of the 20th International Pig Veterinary Science Congress Durban, South Africa: Congress.
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.).
Bolt, J.H. & van der Gaag, L.C. (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) (8 p.).
Kwisthout, J.H.P. & van der Gaag, L.C. (09.07.2008). 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) (8 p.). 24th Conference on Uncertainty in Artificial Intelligence.
van der Gaag, L.C., 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 (Eds.), Proceedings of the 20th International Pig Veterinary Science Durban, South Africa: Congress.
  2007 - Scholarly publications
Bolt, J.H. & van der Gaag, L.C. (2007). Decisiveness in loopy propagation. In P. Lucas, J.A. Gámez & A. Salmeron (Eds.), Advances in Probabilistic Graphical Models (pp. 153-173) (20 p.). Berlin: Springer.
Sent, D. & van der Gaag, L.C. (2007). Enhancing automated test selection in probabilistic networks. In R. Bellazzi, A. Abu-Hanna & J. Hunter (Eds.), Artificial Intelligence in Medicine - Proceedings 11th Conference on Artificial Intelligence in Medicine, AIME 2007 Amsterdam, The Netherlands, July 7-11, 2007 (pp. 331-335) (5 p.). Berlin, Heidelberg: Springer.
de Waal, P.R. & van der Gaag, L.C. (2007). Inference and learning in multi-dimensional Bayesian network classifiers. In K. Mellouli (Eds.), European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 501-511) (11 p.). Berlin Heidelberg: Springer.
Tabachneck-Schijf, H.J.M. & van der Gaag, L.C. (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) (10 p.). Vancouver.
Sent, D. & van der Gaag, L.C. (2007). On the behaviour of information measures for test selection. In R. Bellazzi, A. Abu-Hanna & J Hunter (Eds.), Artifical Intelligence in Medicine - Proceedings 11th Conference on Artificial Intelligence in Medicine, AIME 2007 Amsterdam, The Netherlands, July 7-11, 2007 (pp. 316-325) (10 p.). Berlin, Heidelberg: Springer.
Helsper, E.M. & van der Gaag, L.C. (2007). Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain. Knowledge Engineering Review, 22, (pp. 67-86) (20 p.).
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.
  2007 - Professional publications
Charitos, T., de Waal, P.R. & van der Gaag, L.C. (2007). Convergence in Markovian models with implications for efficiency of inference. International Journal of Approximate Reasoning, 46 (2), (pp. 300-319) (20 p.).
  2006 - Scholarly publications
Charitos, T., Visscher, S., van der Gaag, L.C., 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) (6 p.).
Sent, D. & van der Gaag, L.C. (2006). Automated test selection in decision-support systems: a case study in oncology. Ubiquity: Technologies for Better Health in Aging Societies - Proceedings of MIE2006 (pp. 491-496) (6 p.). IOS Press, MIE2006.
Geenen, P.L., Elbers, A.R.W., van der Gaag, L.C. & Loeffen, W.L.A. (2006). Development of a probabilistic network for clinical detection of classical swine fever. Proceedings of the 11th Symposium of the International Society for Veterinary Epidemiology and Economics (pp. 667-669) (3 p.). Cairns, Australia.
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.
Feelders, A.J. & van der Gaag, L.C. (2006). Learning Bayesian network parameters under order constraints. International Journal of Approximate Reasoning, 42, (pp. 37-53) (17 p.).
van der Gaag, L.C. & de Waal, P.R. (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) (8 p.). Prague.
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.
Bolt, J.H. & van der Gaag, L.C. (2006). Preprocessing the MAP problem. In J. Vomlel M. Studeny (Eds.), Proceedings of the Third European Workshop on Probabilistic Graphical Models (pp. 51-58) (8 p.). Prague.
van der Gaag, L.C. & Almond, R. (2006). Proceedings of the 4th Bayesian Modelling Applications Workshop: Bayesian Models Meet Cognition. Utrecht: Universiteit Utrecht.
Charitos, T. & van der Gaag, L.C. (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) (8 p.). Corvallis: AUAI Press.
Charitos, T. & van der Gaag, L.C. (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) (6 p.). AAAI Press.
van der Gaag, L.C., 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) (7 p.).
  2005 - Scholarly publications
Charitos, T., van der Gaag, L.C., 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) (6 p.). Aberdeen.
Helsper, E.M., van der Gaag, L.C., Feelders, A.J., Loeffen, W.L.A., Geenen, P.L. & Elbers, A.R.W. (2005). Bringing order into Bayesian-network construction. Proceedings of the Third International Conference on Knowledge Capture (pp. 121-128) (8 p.). New York: ACM Press.
Geenen, P.L. & van der Gaag, L.C. (2005). Developing a Bayesian network for clinical diagnosis in veterinary medicine: from the individual to the herd. Proceedings of the Third Bayesian Modelling Applications Workshop, held in conjunction with the Twenty-first Conference on Uncertainty in Artificial Intelligence Edinburgh.
Sent, D., van der Gaag, L.C., 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), (pp. 543-561) (19 p.).
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.
Sent, D. & van der Gaag, L.C. (2005). Generalised reliability characteristics for probabilistic networks. Artificial Intelligence in Medicine, 34, (pp. 41-52) (12 p.).
Helsper, E.M. & van der Gaag, L.C. (2005). Generic knowledge structures for probabilistic-network engineering. Proceedings of the Third Bayesian Modelling Applications Workshop, held in conjunction with the Twenty-first Conference on Uncertainty in Artificial Intelligence Edinburgh.
Schrage, M., IJzendoorn, A.F. & van der Gaag, L.C. (2005). Haskell ready to Dazzle the real world. Proceedings of the 2005 ACM SIGPLAN Workshop on Haskell (pp. 17-26) (10 p.). New York: ACM Press.
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.).
Feelders, A.J. & van der Gaag, L.C. (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) (8 p.). Corvallis: AUAI Press.
Geenen, P.L., van der Gaag, L.C., 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) (8 p.). Nairn,Schotland.
Charitos, T., de Waal, P.R. & van der Gaag, L.C. (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) (6 p.).
de Waal, P.R. & van der Gaag, L.C. (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) (8 p.). Corvallis: AUAI Press.
  2004 - Scholarly publications
Drugan, M.M. & van der Gaag, L.C. (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) (2 p.). Amsterdam: IOS Press.
Geenen, P.L., van der Gaag, L.C., 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) (8 p.). Groningen: University of Groningen.
Bolt, J.H. & van der Gaag, L.C. (2004). Decisiveness in loopy propagation. In P. Lucas (Eds.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (pp. 25-32) (8 p.).
van der Gaag, L.C. & 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) (2 p.). Amsterdam: IOS Press, gaag04.
Helsper, E.M., van der Gaag, L.C. & Groenendaal, F. (2004). Designing a procedure for the acquisition of probability constraints for Bayesian networks. In Motta E. et al (Eds.), Engineering Knowledge in the Age of the Semantic Web (pp. 280-292) (13 p.). Berlin: Springer-Verlag Berlin Heidelberg 2004, 14th International Conference EKAW 2004.
Lucas, P.J.F., van der Gaag, L.C. & Abu-Hanne, A. (2004). Editorial: Bayesian models in biomedicine and health-care. Artificial Intelligence in Medicine, 30 (3), (pp. 201-214) (14 p.).
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.
Feelders, A.J. & van der Gaag, L.C. (2004). Learning Bayesian Network Parameters Under Order Constraints. In P. Lucas (Eds.), Proceedings of the second European workshop on probabilistic graphical models (PGM'04) (pp. 73-80) (8 p.). pgm04.
van der Gaag, L.C., 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) (8 p.). AUAI Press, uai04.
Bolt, J.H. & van der Gaag, L.C. (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) (8 p.).
Geenen, P.L., van der Gaag, L.C., 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) (12 p.). Heidelberg: Springer.
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.).
Blanco, R., van der Gaag, L.C., Inza, I. & Larranaga, P. (2004). Selective classifiers can be too restrictive: a case-study in oesophageal cancer. Proceedings of the Fifth International Symposium on Biological and Medical Data Analysis (pp. 212-223) (12 p.). Heidelberg: Springer.
Charitos, T. & van der Gaag, L.C. (2004). Sensitivity properties of Markovian models. Advances in Intelligent Systems - Theory and Applications. IEEE Computer Society.
de Waal, P.R. & van der Gaag, L.C. (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) (8 p.). Arlington: UAI Press.
Bolt, J.H. & van der Gaag, L.C. (2004). The convergence error in loopy propagation. International Conference on Advances in Intelligent Systems - Theory and Applications IEEE Computer Society.
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 - Scholarly publications
van Dijk, S.F., van der Gaag, L.C. & Thierens, D. (2003). A Skeleton-Based Approach to Learning Bayesian Networks from Data. Lecture Notes in Computer Science, Volume 2838: Proceedings of the Seventh Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 132-143) (12 p.). Springer, DiGaTh03.
van Dijk, S.F., Thierens, D. & van der Gaag, L.C. (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) (12 p.). Springer, DiThGa03.
van Rijsinge, W.P., van der Gaag, L.C., Visseren, F.L.J. & van der Graaf, Y (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) (5 p.). Berlin: Springer.
Sent, D. & van der Gaag, L.C. (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) (10 p.). Berlijn: Springer, pagina 254-263.
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. (2003). Model-based reasoning with qualitative probabilistic networks. In P. Lucas (Eds.), 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) (7 p.).
Sent, D., van der Gaag, L.C., 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, pagina's 510-515; serie:Studies in Health Technology and Informatics; vol.:95; isbn:1586033476.
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.
  2002 - Scholarly publications
Helsper, E.M. & van der Gaag, L.C. (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) (14 p.). London, England: Springer.
Helsper, E.M. & van der Gaag, L.C. (2002). Building Bayesian networks through ontologies. In F. van Harmelen (Eds.), Proceedings of the 15th European Conference on Artificial Intelligence (pp. 680-684) (5 p.). Amsterdam, the Netherlands: IOS Press.
Helsper, E.M. & van der Gaag, L.C. (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) (2 p.). Leuven, Belgium.
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.).
van der Gaag, L.C. & 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) (6 p.). Berlin, Germany: Springer.
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.
Drugan, M.M., Thierens, D. & van der Gaag, L.C. (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) (8 p.). Leuven, Belgium.
Bodlaender, H.L., Van den Eijkhof, F. & van der Gaag, L.C. (2002). On the complexity of the MPA problem in probabilistic networks. In F. van Harmelen (Eds.), Proceedings 15th European Conference on Artificial Intelligence (pp. 675-679) (5 p.). Amsterdam, the Netherlands: IOS Press.
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.
Coupé, V.M.H. & van der Gaag, L.C. (2002). Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36, (pp. 323-356) (34 p.).
Sent, D. & van der Gaag, L.C. (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) (8 p.). Leuven, Belgium.
  2001 - Scholarly publications
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.
Lucas, P.J.F., van der Gaag, L.C. & Abu-Hanna, A. (2001). Bayesian Models in Medicine. Cascais, Portugal: Utrecht University.
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.
Sent, D., van der Gaag, L.C. & 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) (8 p.). Amsterdam, The Netherlands: Universiteit van Amsterdam, CD-ROM: geen boekvorm.
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.
Bodlaender, H.L., Van den Eijkhof, F. & van der Gaag, L.C. (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) (8 p.). Amsterdam: Universiteit van Amsterdam, 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001).
Helsper, E.M. & van der Gaag, L.C. (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) (8 p.). Amsterdam: Universiteit van Amsterdam.
Bodlaender, H.L., Koster, A.M.C., Van den Eijkhof, F. & van der Gaag, L.C. (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) (8 p.). San Francisco: Morgan Kaufmann, 17th Conference on Uncertainty in Artificial Intelligence.
Bodlaender, H.L., Koster, A.M.C., Van den Eijkhof, F. & van der Gaag, L.C. (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) (2 p.). Amsterdam: Universiteit van Amsterdam, 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001).
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 - Scholarly publications
Druzdzel, M.J. & van der Gaag, L.C. (2000). Building probabilistic networks: "Where do the numbers come from ?". IEEE Transactions on Knowledge and Data Engineering, 12, (pp. 481-486) (6 p.). Guest editors Introduction.
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.
Kjaerulff, U. & van der Gaag, L.C. (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) (9 p.). San Francisco: Morgan Kaufmann Publishers.
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.
van der Gaag, L.C. & 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) (12 p.). Berlin: Springer.
Coupé, V.M.H., van der Gaag, L.C. & Habbema, J.D.F. (2000). Sensitivity analysis: an aid for probability elicitation. Knowledge Engineering Review, 15, (pp. 215-232) (18 p.).
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 - Scholarly publications
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.
van der Gaag, L.C. & 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) (10 p.). Bologna, Italy: Pitagora Editrice.
  1998 - Scholarly publications
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..
Coupé, V.M.H. & van der Gaag, L.C. (1998). Exploiting properties of sensitivity analysis for belief betworks. Proceedings of the Second International Symposium on Sensitivity Analysis of Model Output, Joint Research Centre of the European Commission (pp. 75-78) (4 p.).
van der Gaag, L.C. & Meyer, J-J.Ch. (1998). Informational independence: models and normal forms. International Journal of Intelligent Systems, 13, (pp. 83-109) (27 p.).
Coupé, V.M.H. & van der Gaag, L.C. (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) (6 p.). Praque: Union of Czech Mathematicians and Physicists.
van der Wel, F.J.M., van der Gaag, L.C. & Gorte, B.G.H. (1998). Visual exploration of uncertainty in remote sensing classifications. Computers and Geosciences, 24, (pp. 335-343) (9 p.).
  1997 - Scholarly publications
van der Gaag, L.C. & 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) (10 p.). Nederlandse Vereniging voor Kunstmatige Intelligentie (NVKI).
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.
Coupé, V.M.H. & van der Gaag, L.C. (1997). Supporting probability elicitation by sensitivity analysis. In E. Plaza & R. Benjamins (Eds.), Tenth European Workshop on Knowledge Acquisition, Modeling and Management (pp. 335-340) (6 p.). Berlin, Germany: Springer.
  1996 - Scholarly publications
van der Gaag, L.C. (1996). Bayesian belief networks - a guest editor's introduction. AISB quarterly, 94 (8).
van der Gaag, L.C. (1996). Bayesian belief networks: odds and ends. Computer Journal, 39, (pp. 97-113) (17 p.).
van der Gaag, L.C. & Meyer, J-J.Ch. (1996). Characterising normal forms for informational independence. Proceedings of the Sixth International Conference On Information Pocessing and management of Uncertainty in Knowledge-Based Systems (pp. 973-978) (6 p.).
Gorte, B.G.H., van der Gaag, L.C. & van der Wel, F. (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) (12 p.).
van der Gaag, L.C. (1996). On evidence absorption for belief networks. International Journal of Approximate Reasoning, 15, (pp. 265-286) (22 p.).
Meyer, J-J.Ch. & van der Gaag, L.C. (1996). Proceedings of the 8th Dutch Conference on Artificial Intelligence. Utrecht, the Netherlands: Utrecht University.
van der Gaag, L.C. & 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) (12 p.). Utrecht, the Netherlands: Utrecht University.
Jaspers, M.W.M., van der Gaag, L.C., 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) (10 p.).
  0 - Other output
van der Gaag, L.C. 2001 Recipient Evaluation scores for probabilistic networks


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Additional functions and activities


Gegenereerd op 2018-07-23 19:47:13
Full name
prof. dr. ir. L.C. van der Gaag Contact details
Buys Ballotgebouw

Princetonplein 5
Room BBL-510
3584 CC  UTRECHT
The Netherlands


Phone number (direct) +31 30 253 4113
Phone number (department) +31 30 253 9251


Gegenereerd op 2018-07-23 19:47:13
Last updated 19.04.2018