References
- Almond, R. G., and R. J. Mislevy. 1999. “Graphical Models and Computerized Adaptive Testing.” Applied Psychological Measurement 23 (3): 223–237. doi: 10.1177/01466219922031347
- Altendorf, E. E., A. C. Restificar, and T. G. Dietterich. 2005. “Learning from Sparse Data by Exploiting Monotonicity Constraints.” Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005).
- Druzdzel, J., and M. Henrion. 1993. “Efficient Reasoning in Qualitative Probabilistic Networks.” In Proceedings of the Eleventh National Conference on Artificial Intelligence, 548–553. AAAI Press.
- Feelders, A. J. 2007. “A New Parameter Learning Method for Bayesian Networks with Qualitative Influences.” In UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19–22, 117–124.
- Feelders, A. J., and L. C. van der Gaag. 2005. “Learning Bayesian Network Parameters with Prior Knowledge About Context-Specific Qualitative Influences.” Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005).
- Hugin. 2014. “Explorer.” Ver. 8.0. Comput. Software. http://www.hugin.com.
- Johnson, S. G. 2018. “The NLopt Nonlinear-Optimization Package.” Technical Report.
- Kraft, D 1994. “Algorithm 733: TOMP–Fortran Modules for Optimal Control Calculations.” ACM Transactions on Mathematical Software 20 (3): 262–281. doi: 10.1145/192115.192124
- Masegosa, A. R., A. J. Feelders, and L. C. van der Gaag. 2016. “Learning from Incomplete Data in Bayesian Networks with Qualitative Influences.” International Journal of Approximate Reasoning 69: 18–34. doi: 10.1016/j.ijar.2015.11.004
- Nielsen, T. D., and F. V. Jensen. 2007. Bayesian Networks and Decision Graphs (Information Science and Statistics). New York: Springer-Verlag.
- Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco: Morgan Kaufmann.
- Plajner, M., and J. Vomlel. 2016a. “Probabilistic Models for Computerized Adaptive Testing: Experiments.” Technical Report. ArXiv.
- Plajner, M., and J. Vomlel. 2016b. “Student Skill Models in Adaptive Testing.” In Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 403–414. JMLR.org.
- Plajner, M., and J. Vomlel. 2017. “Monotonicity in Bayesian Networks for Computerized Adaptive Testing.” In ECSQARU 2017, edited by Alessandro Antonucci, Laurence Cholvy, and Odile Papini, 125–134. Cham: Springer.
- Powell, M. J. D. 1994. “A Direct Search Optimization Method that Models the Objective and Constraint Functions by Linear Interpolation.” In Advances in Optimization and Numerical Analysis, edited by S. Gomez and J.-P. Hennart, 51–67. Dordrecht: Kluwer Academic.
- Rasch, G. 1960. Studies in Mathematical Psychology: I. Probabilistic Models for Some Intelligence and Attainment Tests. Copenhagen: Danmarks Paedagogiske Institut.
- R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
- Restificar, A. C., and T. G. Dietterich. 2013. Exploiting Monotonicity via Logistic Regression in Bayesian Network Learning. Technical Report. Corvallis, OR : Oregon State University.
- van der Gaag, L. C., H. L. Bodlaender, and A. J. Feelders. 2004. “Monotonicity in Bayesian Networks.” In 20th Conference on Uncertainty in Artificial Intelligence (UAI '04), 569–576.
- van der Gaag, L. C., and P. de Waal. 2006. “Multi-dimensional Bayesian Network Classifiers.” Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM06), Prague, 107–114.
- van der Linden, W. J., and C. A. W. Glas. 2000. Computerized Adaptive Testing: Theory and Practice. Vol. 13. Dordrecht: Kluwer Academic.
- Wellman, M. P 1990. “Fundamental Concepts of Qualitative Probabilistic Networks.” Artificial Intelligence 44 (3): 257–303. doi: 10.1016/0004-3702(90)90026-V