Edited proceedings:

  1. C.J. Butz and P. Lingras (Eds.), Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, Lecture Notes in Artificial Intelligence 6657, Springer, Berlin, 2011.
  2. A. An, J. Stefanowski, S. Ramanna, C.J. Butz, W. Pedtycz and G. Wang (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 11th International Conference, RSFDGrC 2007, Lecture Notes in Artificial Intelligence 4482, Springer, Berlin, 2007.

Invited Papers:

  1. C.J. Butz, Evaluating Probabilistic Inference Techniques: a Question of "When," not "Which," the 5th International Conference on Scalable Uncertainty Management (SUM'2011), to appear, 2011.
  2. C.J. Butz, Current Trends in Bayesian Network Inference, the 2nd Indian International Conference on Artificial Intelligence (IICAI-07), 1186 - 1205, 2007.

Papers in Refereed Journals:

  1. C.J. Butz, K. Konkel and P. Lingras, Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination, International Journal of Approximate Reasoning, Vol. 52, 948-959, 2011.
  2. H.D. Hadjistavropoulos, M. Thompson, M. Ivanov, C. Drost, C.J. Butz, B. Klein and D.W. Austin, Considerations in the Development of a Therapist-Assisted Internet Cognitive Behavior Therapy Service, Professional Psychology: Research and Practice, Vol. 42, No. 6, 463-471, 2011.
  3. P. Lingras and C.J. Butz, Conservative and Aggressive Rough SVR Modeling, Theoretical Computer Science, Vol. 412, 5885-5901, 2011.
  4. C.J. Butz, S. Hua, K. Konkel and H. Yao, Join Tree Propagation with Prioritized Messages, Networks, Vol. 55, No. 4, 350-359, 2010.
  5. P. Lingras and C.J. Butz, Rough Support Vector Regression, European Journal of Operational Research, Vol. 206, No. 2, pp. 445-455, 2010.
  6. C.J. Butz, J. Chen, K. Konkel and P. Lingras, A Formal Comparison of Variable Elimination and Arc Reversal in Bayesian Network Inference, Intelligent Decision Technologies, Vol. 3, No. 3, 173--180, 2009.
  7. C.J. Butz, S. Hua, J. Chen and H. Yao, A Simple Graphical Approach for Understanding Probabilistic Inference in Bayesian networks, Information Sciences, Vol. 179, 699-716, 2009.
  8. C.J. Butz, H. Yao and S. Hua, A Join Tree Probability Propagation Architecture for Semantic Modeling, Journal of Intelligent Information Systems, Vol. 33, No. 2, 145-178, 2009.
  9. P. Lingras and C.J. Butz, Rough Set based 1-v-1 and 1-v-r Approaches to Support Vector Machine Multi-classification, Information Sciences, Vol. 177, 3782-3798, 2007.
  10. C.J. Butz, W. Yan and B. Yang, An Efficient Algorithm for Inference in Rough Set Flow Graphs, Transactions on Rough Sets, Vol. 5, 102-122, 2006.
  11. C.J. Butz, S. Hua and R.B. Maguire, A Web-based Bayesian Intelligent Tutoring System for Computer Programming, Web Intelligence and Agent Systems: An International Journal, Vol. 4, No. 1, 77-97, 2006.
  12. S.K.M. Wong and C.J. Butz, Constructing the Dependency Structure of a Multi-Agent Probabilistic Network, IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 3, 395-415, May 2001.
  13. S.K.M. Wong, C.J. Butz, and D. Wu, On the Implication Problem for Probabilistic Conditional Independency, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 30, No. 6, 785-805, November 2000.
  14. S.K.M. Wong and C.J. Butz, A Bayesian Approach to User Profiling in Information Retrieval, Technology Letters, Vol. 4, No. 1, 50-56, 2000.
  15. S.K.M. Wong, C.J. Butz and Y. Xiang, Automated Database Schema Design Using Mined Data Dependencies, Journal of the American Society for Information Science, Vol. 49, No. 5, 455-470, April 1998.

Refereed Book Chapters:

  1. C.J. Butz, W. Yan, P. Lingras and Y.Y. Yao, The CPT Structure of Variable Elimination in Discrete Bayesian Networks, Advances in Intelligent Information Systems, SCI 265, Z.W. Ras and L.S. Tsay (Eds.), Springer, 245-257, 2010.
  2. C.J. Butz, S. Hua and R.B. Maguire, ``Web-based Bayesian Intelligent Tutoring Systems,'' Evolution of WEB in an Artificial Intelligence Environment, SCI 130, R. Nayek and L.C. Jain (Eds.), Springer-Verlag, 223-244, 2008.
  3. P. Lingras, S. Asharaf and C.J. Butz, ``Rough Clustering,'' Handbook of Granular Computing, W. Pedrycz, A. Skowron and V. Kreinovich (Eds.), Wiley, 969-985, 2008.
  4. C.J. Butz and W. Yan, ``Current Trends in Rough Set Flow Graphs,'' Rough Computing: Theories, Technologies and Applications, A.E. Hassanien, Z. Suraj, D. Slezak and P. Lingras (Eds.), Information Science Reference, 152-161, 2007.
  5. H. Yao, C.J. Butz and H.J. Hamilton, ``Causal Discovery,'' Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach (Eds.), Springer, pp. 945-955, 2005.
  6. C.J. Butz and M.J. Sanscartier, ``Towards Web Search Using Contextual Probabilistic Independencies'', Computational Web Intelligence: Intelligent Technology for Web Applications, Y. Zhang, A. Kandel, T.Y. Lin and Y.Y. Yao (Eds.), World Scientific, 149-166, 2004.
  7. S.K.M. Wong and C.J. Butz, ``The Membership Problem for Probabilistic and Data Dependencies'', in Technologies for Constructing Intelligent Systems, B. Bouchon-Meunier, L. Magdalena, and R.R. Yager (Eds.), Springer Verlag, Vol. 2, 73-84, 2002.
  8. S.K.M. Wong, Y.Y. Yao and C.J. Butz, ``Granular Information Retrieval'', Soft Computing in Information Retrieval: Techniques and Applications. F. Crestani and G. Pasi (Eds.), Springer Verlag, 317-331, 2000.

Papers in Refereed Conference Proceedings:

  1. C.J. Butz, A.L. Madsen and K. Williams, Using Four Cost Measures to Determine Arc Reversal Orderings, 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 110--121, 2011.
  2. C.J. Butz and W. Yan, The Semantics of Intermediate CPTs in Variable Elimination, Fifth European Workshop on Probabilistic Graphical Models (PGM), 41-49, 2010.
  3. C.J. Butz, K. Konkel and P. Lingras, Join Tree Propagation utilizing both Arc Reversal and Variable Elimination, Twenty Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), 523--528, 2009.
  4. C.J. Butz, J. Chen, K. Konkel and P. Lingras, A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference, Twenty Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), 571--572, 2009.
  5. C.J. Butz, W. Yan, P. Lingras, K. Konkel and Y. Yao, On Variable Elimination in Discrete Bayesian Network Inference, 9th World Meeting of the International Society for Bayesian Analysis (ISBA08), 96--97, 2008.
  6. C.J. Butz, P. Lingras and K. Konkel, A Web-based Interface for Hiding Bayesian Network Inference, 17th International Symposium on Methodologies for Intelligent Systems (ISMIS08), 612--617, 2008.
  7. P. Lingras and C.J. Butz, Precision and Recall in Rough Support Vector Machines, the IEEE International Conference on Granular Computing, 654--658, 2007.
  8. C.J. Butz and S. Hua, An Improved LAZY-AR Approach to Bayesian network Inference, Nineteenth Canadian Conference on Artificial Intelligence (AI), 183--194, 2006.
  9. C.J. Butz and F. Fang, Sophisticated Indexes for Implementing Probabilistic Expert Systems, the 2nd Indian International Conference on Artificial Intelligence (IICAI-05), 609--620, 2005.
  10. C.J. Butz and P. Lingras, On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency, the 2nd Indian International Conference on Artificial Intelligence (IICAI-05), 2464--2475, 2005.
  11. C.J. Butz and F. Fang, Modelling Multiagent Bayesian networks with Inclusion Dependencies, the IEEE/WIC/ACM Conference on Intelligent Agent Technology (IAT05), 455--458, 2005.
  12. P. Lingras and C.J. Butz, Interval Set Representations of 1-v-r Support Vector Machine Multi-classifiers, the IEEE International Conference on Granular Computing, 193--198, 2005.
  13. C.J. Butz, W. Yan and B. Yang, The Computational Complexity of Inference using Rough Set Flow Graphs, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 1, 335--344, 2005.
  14. D. Wu and C.J. Butz, On the Computational Complexity of Probabilistic Inference in Singly Connected Bayesian networks, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 1, 581--590, 2005.
  15. P. Lingras and C.J. Butz, Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 2, 166--173, 2005.
  16. C.J. Butz and F. Fang, Incorporating Evidence in Bayesian networks with the Select Operator, Eighteenth Canadian Conference on Artificial Intelligence (AI), 297--301, 2005.
  17. D.H. Hepting and C.J. Butz, An Integrated Approach to Discovery in Complex Information Spaces, Second International Workshop on Web-based Support Systems, 67--74, 2004.
  18. C.J. Butz, S. Hua and R.B. Maguire, A Web-based Intelligent Tutoring System for Computer Programming, the IEEE/WIC/ACM Conference on Web Intelligence (WI04), 159--165, 2004.
  19. Y. Yao, J. Yao, C.J. Butz, P. Lingras and D. Jutla, Web-based Support Systems: a Report of the WIC Canada Research Centre, the IEEE/WIC/ACM Conference on Web Intelligence (WI04), 787--788, 2004.
  20. H. Yao, H. Hamilton and C.J. Butz, A Foundational Approach for Mining Itemset Utilities from Databases, 2004 SIAM International Conference on Data Mining (SIAMDM04), 482--486, 2004.
  21. C.J. Butz and J. Liu, On the Implication Problem in Granular Knowledge Systems, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 63-68, 2004.
  22. C.J. Butz and P. Lingras, Granular Jointree Probability Propagation, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 69-72, 2004.
  23. P. Lingras and C.J. Butz, Interval Set Classifiers using Support Vector Machines, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 707-710, 2004.
  24. C.J. Butz, H. Yao and H. Hamilton, Towards Jointree Propagation with Conditional Probability Distributions, the 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC04), 368-377, 2004.
  25. C.J. Butz, S. Hua and R.B. Maguire, Bits: a Bayesian Intelligent Tutoring System for Computer Programming, the 9th Western Canadian Conference on Computing Education (WCCCE04), 179-186, 2004.
  26. C.J. Butz and H. Geng, Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks, 14th International Symposium on Methodologies for Intelligent Systems (ISMIS03), 544--553, 2003.
  27. C.J. Butz and J. Liu, A Query Processing Algorithm for Hierarchical Markov Networks, 2nd Annual Asia-Pacific Conference on Web Intelligence (WI03), 588--592, 2003.
  28. C.J. Butz, A General Coarsening Method for Granular Probabilistic Networks, 7th International Conference on Computer Science and Informatics (CSI03), 462--465, 2003.
  29. C.J. Butz, Constructing the Maximal Prime Decomposition of Bayesian Networks, 7th International Conference on Computer Science and Informatics (CSI03), 458--461, 2003.
  30. C.J. Butz, S.K.M. Wong and D. Wu, A New Inference Axiom for Probabilistic Conditional Independence, Sixteenth Canadian Conference on Artificial Intelligence (AI), 568--574, 2003.
  31. S.K.M. Wong, D. Wu and C.J. Butz, Probabilistic Reasoning in Bayesian Networks: a Relational Database Approach, Sixteenth Canadian Conference on Artificial Intelligence (AI), 583--590, 2003.
  32. C.J. Butz, Q. Hu and X.D. Yang, Critical Remarks on the Maximal Prime Decomposition of Bayesian Networks, 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC03), 682--685, 2003.
  33. C.J. Butz, H. Yao and H. Hamilton, A Non-Local Coarsening Result in Granular Probabilistic Networks, 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC03), 686-689, 2003.
  34. H. Yao, H. Hamilton and C.J. Butz, FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences, 2002 IEEE International Conference on Data Mining (ICDM02), 729--732, 2002.
  35. S.K.M. Wong, D. Wu and C.J. Butz, Triangulation of Bayesian networks: a Relational Database Perspective, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 389--396, 2002.
  36. C.J. Butz and M.J. Sanscartier, Properties of Weak Conditional Independence, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 349--356, 2002.
  37. C.J. Butz and M.J. Sanscartier, Acquisition Methods for Contextual Weak Independence, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 339--343, 2002.
  38. C.J. Butz and M.J. Sanscartier, A Method for Detecting Context-Specific Independence in Conditional Probability Tables, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 344--348, 2002.
  39. C.J. Butz and M.J. Sanscartier, On the Role of Contextual Weak Independence in Probabilistic Inference, Fifteenth Canadian Conference on Artificial Intelligence (AI), 185--194, 2002.
  40. C.J. Butz, Exploiting Contextual Independencies in Web Search and User Profiling, World Congress on Computational Intelligence (WCCI), 1051--1056, 2002.
  41. C.J. Butz, Critical Remarks on Bayesian Network Libraries, 6th International Conference on Computer Science and Informatics (CSI02), 402-406, 2002.
  42. C.J. Butz, On Axiomatizing Probabilistic Conditional Independencies in Bayesian Networks, 1st Annual Asia-Pacific Conference on Web Intelligence (WI01), 131-135, 2001.
  43. S.K.M. Wong, C.J. Butz and D. Wu, On Undirected Representations of Bayesian Networks, ACM SIGIR Workshop on Mathematical/Formal Models in Information Retrieval (MF/IR), 52-59, 2001.
  44. S.K.M. Wong and C.J. Butz, A Bayesian Approach to User Profiling in Information Retrieval, ACM SIGIR Workshop on Mathematical/Formal Models in Information Retrieval (MF/IR), 50-56, 2000. (Also published in Technology Letters, Vol. 4, No. 1, 50-56, 2000.)
  45. S.K.M. Wong and C.J. Butz, A Comparative Study of Noncontextual and Contextual Dependencies, 12th International Symposium on Methodologies for Intelligent Systems (ISMIS00), 247-255, 2000.
  46. S.K.M. Wong and C.J. Butz, The Implication of Probabilistic Conditional Independence and Embedded Multivalued Dependency, 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU00), 876--881, 2000.
  47. C.J. Butz and S.K.M. Wong, A Local Nest Property in Granular Probabilistic Networks, 5th Joint Conference on Information Sciences (JCIS00), Volume 1, Association for Intelligent Machinery, Inc., 158-161, 2000.
  48. S.K.M. Wong and C.J. Butz, Rough Sets for Uncertainty Reasoning, 2nd International Conference on Rough Sets and Current Trends in Computing (RSCTC00), 473-480, 2000.
  49. S.K.M. Wong and C.J. Butz, Contextual Weak Independence in Bayesian Networks, 15th Conference on Uncertainty in Artificial Intelligence (UAI99), Morgan Kaufmann, 670-679, 1999.
  50. C.J. Butz, S.K.M. Wong and Y.Y. Yao, On Data and Probabilistic Dependencies, IEEE Canadian Conference on Electrical and Computer Engineering (CCECE'99), IEEE Press, 1692-1697, 1999.
  51. C.J. Butz and S.K.M. Wong, Recovery Protocols in Multi-Agent Probabilistic Reasoning Systems, International Database Engineering and Applications Symposium (IDEAS'99), IEEE Press, 302-310, 1999.
  52. Y.Y. Yao, S.K.M. Wong and C.J. Butz, On Information-Theoretic Measures of Attribute Importance, 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PKDD'99), 133-137, 1999.
  53. S.K.M. Wong and C.J. Butz, A Probabilistic Network Versus Decision Rules, 6th International Conference on Rough Sets, Data Mining and Granular Computing (RSDMGrC98), Association for Intelligent Machinery, Inc., 310-315, 1998.
  54. S.K.M. Wong and C.J. Butz, A Method for Constructing the Dependency Structure of a Probabilistic Network, 7th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU98), 1462-1469, 1998.
  55. S.K.M. Wong and C.J. Butz, Probabilistic Reasoning in a Distributed Multi-Agent Environment, 3rd International Conference on Multi-Agent Systems (ICMAS98), IEEE Press, 341-348, 1998.
  56. S.K.M. Wong and C.J. Butz, Equivalent Characterization of a Class of Conditional Probabilistic Independencies, 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC98), 338-345, 1998.
  57. S.K.M. Wong, C.J. Butz and Y. Xiang, A Method for Implementing a Probabilistic Model as a Relational Database, 11th Conference on Uncertainty in Artificial Intelligence (UAI95), Morgan Kaufmann Publishers, 556-564, 1995.

Technical Reports:

  1. C.J. Butz, H. Yao and S. Hua, A Join Tree Probability Propagation Architecture for Semantic Modelling, University of Regina, Computer Science Department, Technical Report CS-2004-10, November, 2004, ISBN 0-7731-0499-2.
  2. H. Yao, H. Hamilton and C.J. Butz, FD_MINE: Discovering Functional Dependencies in a Database Using Equivalences, University of Regina, Computer Science Department, Technical Report CS-02-04, August, 2002, ISBN 0-7731-0441-0.
  3. S.K.M. Wong, C.J. Butz and D. Wu, A Relational Knowledge System, University of Regina, Computer Science Department, Technical Report CS-01-04, January, 2001, ISSN 0828-3494, ISBN 0-7731-0417-8.
  4. S.K.M. Wong, C.J. Butz and D. Wu, On the Implication Problem for Probabilistic Conditional Independency, University of Regina, Computer Science Department, Technical Report CS-99-03, September, 1999, ISSN 0828-3494, ISBN 0-7731-0390-2.

Graduate Work

Ph.D. thesis: The Relational Database Theory of Bayesian Networks.
M.Sc. thesis: Probabilistic Reasoning Using an Extended Relational Data Model.