Bibliography for Bayesian Networks in Educational Assessment

This is the bibliography for the book Bayesian Networks in Educational Assessment with authors Russell Almond, Robert Mislevy, Linda Stienberg, David Williamson, and Duanli Yan to be published by Springer. We are posting it here (a) in the hopes that it will be of use to people, and (b) in the hopes that we will get updates for technical reports which have been published as papers and corrections for mistakes.

This page is semi-automatically generated from the BibTeX database which is also on-line. A PDF version is also available.

• R. Adams, M[ark] R. Wilson, and W-C. Wang. The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21:1–23, 1997.
• R. J. Adler. Markov random fields. In Samuel Kotz and Norman L. Johnson, editors, Encylopedia of Statistical Sciences, volume 5, pages 270–273. Wiley, 1985.
• H. Akaike. Information theory and an extension of the maximum likelihood principle. In B. N. Petrov and F. Cáki, editors, Proceedings of the 2nd International Symposium on Information Theory, pages 267–281, 1973.
• J. H. Albert. Bayesian estimation of normal ogive item response curves using gibbs sampling. Journal of Educational Statistics, 17:251–269, 1992.
• R.G. Almond and C-T. A. Kong. Some heuristics for building an optimal tree of cliques from a graph or hypergraph. Research Report 329, University of Chicago, Department of Statistics, 1991. Finn Jensen dubbed the tree produced by this algorithm as the "Almond Tree.".
• Russell G. Almond and Robert J. Mislevy. Graphical models and computerized adaptive testing. Applied Psychological Measurement, 23:223–238, 1999.
• Russell G. Almond and Joris Mulder. Models for local dependence among observable outcome variables. In Third Annual Bayesian Application Workshop at the 2005 Uncertainty in Artificial Intelligence Application Workshop, 2005.
• R.G. Almond, J.M. Bradshaw, and D. Madigan. Reuse and sharing of graphical belief network components. In P. Cheeseman and W. Oldford, editors, Selecting Models from Data: Artificial Intelligence and Statistics IV, pages 113–122. Springer-Verlag, 1994.
• R.G. Almond, R.J. Miselvy, and L.S. Steinberg. A multivariate frameork for educational testing. In Taipei Interanational Statistics Symposium, 1998.
• R.G. Almond, E. Herskovits, R.J. Mislevy, and L.S. Steinberg. Transfer of information between system and evidence models. In D. Heckerman and J. Whittaker, editors, Artificial Intelligence and Statistics 99, pages 181–186, 1999.
• R.G. Almond, L. Dibello, F. Jenkins, R.J. Mislevy, D. Senturk, L.S. Steinberg, and D. Yan. Models for conditional probability tables in educational assessment. In T. Jaakkola and T. Richardson, editors, Artificial Intelligen ce and Statistics 2001, pages 137–143. Morgan Kaufmann, 2001.
• R.G. Almond, L. DiBello, F. Jenkins, R.J. Mislevy, D. Senturk, L.S. Steinberg, and D. Yan. Models for conditional probability tables in educational assessment. In Jaakkola and Richardson, editors, Artificial Intelligence and Statistics 2001 Jaakkola and Richardson, pages 137–143. Morgan Kaufmann, 2001.
• R.G. Almond, L.S. Steinberg, and R.J. Mislevy. The four process assessment delivery architecture. In Cognition and Assessment: Theory to Practice. University of Maryland, August 2001.
• R.G. Almond, A. Matukhin, L. Steinberg, S. Sinharay, D.M. Williamson, and D. Yan. A framework for calibrating evidence models. Unpublished StatShop documentation, 2002.
• R.G. Almond, A. Matukhin, L. Steinberg, D.M. Williamson, and D Yan. A framework for evidence accumulation. Unpublished StatShop design documention, 2002.
• R.G. Almond, L.S. Steinberg, and R.J. Mislevy. Enhancing the design and delivery of assessment systems: A four-process architecture. Journal of Technology, Learning, and Assessment, 1(5):(online), 2002.
• Russell G. Almond, Linda S. Steinberg, and Robert J. Mislevy. Enhancing the design and delivery of assessment systems: A four-process architecture. Journal of Technology , Learning, and Assessment, 1:(online), 2002.
• Russell G. Almond, Linda S. Steinberg, and Robert J. Mislevy. A framework for reusing assessment components. In H. Yanai, Okada. A., K. Shigemasu, Y. Kano, and J. J. Meulman, editors, New Developments in Psychometrics, pages 281–288. Springer, 2002.
• Russell G. Almond, Robert J. Mislevy, and Duanli Yan. Using anchor sets to identify scale and location of latent variables. Ets research report, Educational Testing Service, Draft. Draft not yet submitted for review.
• Russell G. Almond, Linda S. Steinberg, and Sinharay. Method and system for calibrating evidence models. U.S. Patent Application, Pending. Attorney Docket No. 122467.03601, Pepper Hamilton.
• Russell G. Almond, Duanli Yan, Alexander Matukhin, and Denise Chang. Statshop testing. Research memorandum, Educational Testing Service, To Appear. Currently in copy editing.
• R.G. Almond, D. Yan, and L.A. Hemat. Simulation studies with a four proficiency Bayesian network model. Ets research report, Educational Testing Service, Under Review . Under Review.
• Russell G. Almond, Joris Mulder, Lisa A. Hemat, and Duanli Yan. Models for local dependence among observable outcome variables. Ets research report, Educational Testing Service, Under Review .
• R.G. Almond. Lack of information based control in expert systems.? In Artificial Intelligence Frontiers in Statistics: AI and Statistics III, pp 82-89. Chapman and Hall, 1993.
• R.G. Almond. Hypergraph grammars for knowledge based model construction. StatSci Research Report 23, 1995.
• Russell G. Almond. Graphical Belief Modeling. Chapman and Hall, 1995.
• R. G. Almond. Question and test interoperability in the new millenium. In International Meeting of the Psychometric Society, 2001.
• Russell G Almond. Using evidence models to aggregate information. White Paper developed for ONR call for suggestions about the future of assessment., 2002.
• S.K. Andersen, K.G. Olesen, F.V. Jensen, and F. Jensen. Hugin — a shell for building Bayesian belief universes for expert systems. In IJCAI '89, 1989. Reprinted in Shafer and Pearl [1990].
• S.A. Andersen, D. Madigan, and M.D. Perlman. An alternative markov property for chain graphs. In Uncertainty in Artificial Intelligence, Proceedings of the Twelfth Conference. Morgan Kaufmann, 1996.
• S.A. Andersen, D. Madigan, and M.D. Perlman. A characterization of markov equivalence classes for acyclic digraphs. Annals of Statistics, 25:505–41, 1996.
• P. Bacchetti, M.R. Segal, and N.P. Jewell. Backcalculation of hiv infection rates (with discussion). Statistical Sciences, 8:82–119, 1993.
• P.E. Barton. Parsing the achievement gap: Baselines for tracking progress. Policy information center report, ETS, 2003.
• J.T. Behrens, R.J. Mislevy, M. Bauer, D.M. Williamson, and R. Levy. Introduction to evidence centered design and lessons learned from its application in a global e-learning program. International Journal of Measurement, 4:295–301, 2004.
• R. Bellazi, C. Berzuini, S. Quaglini, D. Spiegelhalter, and M. Leaning. Cytotoxic chemotherapy monitoring using stochastic simulation on graphical models. In Fieschi Stefanelli, Hasman and Talmon, editors, Proceedings of the 3rd Conference on Artificial Intelligence in Medicine. Springer-Verlag, 1991.
• J.O. Berger. Statistical Decision Theory and Bayesian Analysis,. Springer-Verlag, New York, 1985.
• U. Bertelè and F. Brioschi. Nonserial Dynamic Programming. Academic Press., 1972.
• C. Berzuini, D.J. Spiegelhalter, and R. Bellazi. Bayesian networks applied to therapy monitoring. In D'Ambrosio, Smets, and Bonissone, editors, Uncertainty in Artificial Intelligence: Proceedings of the 7th Conference,. Morgan Kaufmann, 1991.
• Nicky Best, Mary Kathryn Cowles, and Karen Vines. CODA: Convergence Diagnosis and Output Analysis Software for Gibbs sampling output Version 0.30, 1996.
• Y.M. Bishop, S. Fienberg, and P. Holland. Discrete Multivariate Analysis. M.I.T. Press, 1975.
• R.D. Bock and M. Aitkin. Marginal maximum likelihood estimation of item parameters: An application of an em-algorithm. Psychometrika, 46:443–459, 1981.
• K.A. Bollen. Structural Equations with Latent Variables. John Wiley, 1989.
• Craig Boutilier, Thomas Dean, and Steve Hanks. Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 11:1–94, 1999.
• G.E.P. Box and G.C. Tiao. Bayesian Inference in Statistical Analysis. John Wiley and Sons, 1973. Reprinted in Wiley Classics Library Edition, 1992.
• G.E.P. Box. Science and statistics. JASA, 71:791–799, 1976. Source of the quote "All models are models are false, but some are useful.".
• G. E. P. Box. Sampling and Bayes inference in scientific modelling and robustness (with discussion). JRSS A, 143:383–430, 1980.
• X. Boyen and D. Koller. Tractable inference for complex stochastic process. In In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pages 33–42. UAI-98, 1998.
• E.T. Bradlow, H. Wainer, and X. Wang. A Bayesian random effects model for testlets. Psychometrika, 64:153–168, 1999.
• J. M. Bradshaw, P.D. Holm, J.H. Boose, D. Skuce, and T.C. Lethbridge. Sharable ontologies as a basis for communication and collaboration in conceptual modeling. In Proceedings of the Seventh Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff,Alberta, Canada., 1992.
• A Braga-Illa. A simple approach to the Bayes choice criterion: The method of critical probabilities. Journal of the American Statistical Association,, 59:1227–1230, 1964.
• J.S. Breese, R.P. Goldman, and M.P. Wellman. Introduction to the special section on knowledge-based construction of probabilistic and decision models. IEEE Transactions on System, Man and Cybernetics, 24:1577–1579, 1994.
• L. Breiman, J. H. Friedman, R. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, 1984.
• R.L. Brennan and D.J. Prediger. Coefficient kappa: Some uses, misuses, and alternatives. Technical Bulletin 29, ACT, 1977.
• F.J. Breyer, R.J. Mislevy, L.S. Steinberg, and R.G. Almond. Designing technology-based assessments: It's the evidence for the inferences that are important. In Annual Convention of the Society for Industrial Organizational Psychology, 1999.
• G. W. Brier. Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78:1–3, 1950.
• S. Brooks and A. Gelman. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7:434–55, 1998.
• B. G. Buchanan and E.H. Shortlife. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, 1984.
• W.L. Buntine. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2:159–225, 1994.
• W.L. Buntine. A guide to the literature on learning probabilistic networks from data. IEEE Trans. on Knowledge and Data Engineering, 8:195–210, 1996.
• C. Cannings, E.A. Thompson, and M.H. Skolnick. Probability functions on complex pedigrees. Advances in Applied Probability, 10:26–61, 1978.
• Kathryn M. Chaloner and George T. Duncan. Assessment of a beta prior distribution: Pm elicitation. The Statistician, 32:174–180, 1983.
• P. Cheeseman. Uncertainty in artificial intelligence. In L.N. Kanal and J.F. Lemmer, editors, Probabilistic versus Fuzzy Reasoning, pages 85–102. Elsevier Science Publishers, 1986.
• P. P-S. Chen. The entity-relationship model — toward a unified view of data. ACM Transactions on Database Systems, 1:9–36, 1976.
• D. Chickering. Learning equivalence classes of Bayesian-network structures. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 87–98. Morgan Kaufmann, 1996.
• B.R. Cobb and P.P. Shenoy. Hybrid Bayesian networks with linear deterministic variables. In F. Bacchus and T. Jaakkola, editors, Proceedings of the Twenty-First Conference, pages 136–144. AUAI Press, 2005.
• J.M. Collis, P.G.C. Tapsfield, S.H. Irvine, P.L. Dann, and D. Wright. The british army recruit battery goes operational: From theory to practice in computer-based testing using itemgeneration techniques. International Journal of Selection and Assessment, 3:96–104, 1995.
• Gregory F. Cooper and Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347, 1992.
• R.G. Cowell and A.P. Dawid. Fast retraction of evidence in a probabilistic expert system. Statistics and Computing, 2:36–41, 1992.
• A.P. Cowell, R.G.and Dawid and D.J. Spiegelhalter. Sequential model criticism in probabilstic expert systems. IEEE Transaction of Pattern Analysis and Machine Intelligence, 15:209–129, 1993.
• R.G. Cowell, A.P. Dawid, S.L. Lauritzen, and D.J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer, 1999.
• D.R. Cox and N. Wermuth. Multivariate Dependencies: Models, analysis and interpretation. Chapman and Hall, 1996.
• L.J. Cronbach. Intelligence: Measurement, theory, and public policy. In R.L. Linn, editor, Construct validation after thirty years, pages 147–171. Urbana, IL: University of Illinois Press., 1989.
• B. Daniel, J.-D. Zapata-Rivera, and G. McCalla. A Bayesian computational model of social capital in virtual communities. In Proceedings of the First International Conference on Communities and Technologies: C & T 2003. Kluwer Academic Publishers, 2003.
• J.N.and S.L. Lauritzen Darroch and T.P. Speed. Markov fields and log-linear interaction models for contingency tables. The Annals of Statistics, 8:522–539, 1980.
• A. P. Dawid and S. L. Lauritzen. Hyper-markov laws in the statistical analysis of decomposable graphical models. Annals of Statistics, 21:1272–1317, 1993.
• A.P. Dawid. Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41:1–31, 1979.
• A.P. Dawid. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and Computing, 2:25–36, 1992.
• C.M. Dayton. Latent Class Scaling Analysis. Sage, 1999.
• B. De Finetti. Teoria Delle Probabilità. Wiley classics Library, 1970. Republished as in English as Theory of Probability, 1990.
• T. Dean and K. Kanazawa. A model for reasoning about persistence and causation. Computer Intelligence, 5:142–150, 1989.
• M.H. DeGroot. Optimal Statistical Decisions. McGraw–Hill, 1970.
• A. Dekhtyar, R. Finkel, J. Goldsmith, B. Goldstein, and C. Isenhour. Adaptive decision support for planning under hard and soft constraints. In M. J. Druzdezel and T.-Y. Leong, editors, Challenges to decision support in a changing world: Papers from 2005 AAAI Spring Symposium, 2005.
• N. Dempster, A.P.and Laird and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. JRSS B,, 39:1–38, 1972.
• A.P. Dempster. A generalization of Bayesian inference (with discussion). Journal of the Royal Statistical Society, Series B, 30:205–247, 1968.
• A.P. Dempster. Covariance selection. Biometrics, 28:157–175, 1972.
• P.J. DeVito and J.A. Koenig. Investigating District-Level and Market-Basket Reporting. National Research Council, 2000.
• F.J. Dí ez. Parameter adjustment in Bayes networks. the generalized noisy or-gate. In D. Heckerman and A. Mamdani, editors, In Uncertainty in Artificial Intelligence 93, pages 99–105. Morgan-Kaufmann, 1993.
• L.V. DiBello, W.F. Stout, and L.A. Roussos. Cognitively diagnostic assessment. In P.D. Nichols, S.F. Chipman, and R.L. Brennen, editors, Unified cognitive/psychometric diagnostic assessment likelihood based classification techniques, pages 361–389. Lawrence Erlbaum, 1995.
• D. Draper, J.S. Hodges, E.E. Leamer, C.N. Morris, and D.B. Rubin. A research agenda for assessment and propagation of model uncertainty. Rand Note N-2683-RC, The RAND Corporation, 1987.
• David Draper. Assessment and propagation of model uncertainty. JRSS B, 57:45–98, 1995.
• D. Edwards and S. Kreiner. The analysis of contingency tables by graphical models. Biometrika, 70:553–565, 1983.
• D. Edwards. Hierarchical interaction models. Journal of the Royal Statistical Society, Series B, 52:3–20, 1990.
• D. Edwards. Introduction to graphical modelling. Springer-Verlag., 1995.
• S. Embretson. Construct validity: Construct representation versus nomothetic span. Psychological Bulletin, 93:179–197, 1983.
• S.E. Embretson. Test theory for a new generation of tests. In N. Frederiksen, R.J. Mislevy, and I.I. Bejar, editors, Psychometric models for learning and cognitive processes, pages 125–150. Laurence Erlbaum Associates, 1993.
• S.E. Embretson. A cognitive design system approach to generating valid tests: Application to abstract reasoning. Psychological Methods, 3:380–396, 1998.
• E. S. Epstein. A scoring system for probability forecasts of ranked categories. Journal of Applied Meteorology, 8:985–987, 1969.
• J-C. Falmagne. A latent trait model via a stochastic learning theory for a knowledge space. Psychometrika, 54:283–303, 1989.
• W. Feller. An Introduction to Probability Theory and Its Applications, Third Edition. Wiley, 1968.
• G.H. Fischer. The linear logistic test model as an instrument in educational research. Acta Psychologica, 37:359–374, 1973.
• B. Fischhoff, S. Lichtenstein, P. Slovic, S.L. Derby, and R.L. Keeney. Acceptable Risk. Cambridge University Press, 1981.
• J.L. Fleiss, B. Levin, and M.C. Paik. Statistical Methods for Rates and Proportions. Wiley, 2003.
• J. Flores, J. Gamez, and K.G. Olesen. Incremental compilation of Bayesian networks. In U. Kjæ rulff and C. Meek, editors, Uncertainty in Artificial Intelligence, Proceedings of the Nineteenth Conference (UAI 03), pages 233–240. Morgan Kaufmann, 2003.
• L.T. Frase, M. Chudorow, R.G. Almond, J. Burstein, K. Kukich, R.J. Mislevy, L.S. Steinberg, and K. Singley. Technology and assessment. In H.F. O'Neil and R. Perez, editors, Technology applications in assessment: A learning view, pages 213–244. Erlbaum, 2003.
• D. Geiger and D. Heckerman. Advances in probabilistic reasoning. In In Computing Science and Statistitics: Proceedings of the 23rd Symbosium on the Interface, Interface Foundation of North America, pages 22–29, 1992.
• A.E. Gelfand and A.F.M. Smith. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85:398–409, 1990.
• A. Gelman and D.B. Rubin. Inference from iterative simulation using multiple sequences. Statistical Science, 7:457–511, 1992.
• A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin. Bayesian data analysis. Chapman and Hall, 1995.
• A. Gelman, X.L. Meng, and H.S. Stern. Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica, 6:733–807, 1996.
• S. Geman and D. Geman. Stochastic relaxation, gibbs distributions,and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.
• A. Gertner, C. Conati, and K. VanLehn. Procedural help in andes: Generating hints using a Bayesian network student model. In Proceedings of the Fifteenth National Conference on Artificial Intelligence AAAI-98, pages 106–111. The MIT Press, 1998.
• W.R. Gilks, A. Thomas, and D.J. Spiegelhalter. A language and program for complex Bayesian modelling. The Statistician, 43:169–178, 1994.
• W.R. Gilks, S. Richardson, and D.J. Spiegelhalter. Markov Chain Monte Carlo in Practice. Chapman and Hall, 1996.
• C.A.W. Glas and W.J. van der Linden. Modeling variability in item parameters in cat. In International Meeting of the Psychometrics Society, July 2001.
• R. Glaser, A. Lesgold, and S. Lajoie. Toward a cognitive theory for the measurement of achievement. In R. Ronning, J. Glover, J.C. Conoley, and J. Witt, editors, The influence of cognitive psychology on testing and measurement:The Buros-Nebraska Symposium on measurement and testing, volume 3, pages 41–85. Erlbaum, 1987.
• P. Glasziou and J. Hilden. Test selection measures. Medical Decision Making, 9:133–141, 1989.
• R. P. Goldman and E. Charniak. A lanugage for construction of belief networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15:196–208, 1993.
• I. J. Good and W. Card. The diagnostic process with special reference to errors. Method of Inferential Medicine, 10:176–188, 1971.
• I.J. Good. Probability and the Weighing of Evidence. Charles Griffin, 1950.
• I.J. Good. Rational decisions. JRSS B, 14:104–114, 1952.
• I.J. Good. The appropriate mathematical tools for describing and measuring uncertainty. In C. F. Carter, G.P. Meredith, and G.L.S. Sheckle, editors, Uncertainty and Business Decisions, pages 385–388. University Press, 1954.
• I.J. Good. 46656 varieties of Bayesian. American Statistician, 25:62–63, 1971. Reprinted in Good Thinking University of Minnesota Press, 1983, 20–21.
• I.J. Good. The probabilistic explication of information, evidence, surprise, causality, explanation and utility. In In Proceedings of a Symposium on the Foundations of Statistical Inference, pages 108–141. Holt, Rinehart and Winston, 1971.
• I.J. Good. The Bayesian influence, or how to sweep subjectivism under the carpet. In C. A. Hooker and W. Harper, editors, Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, Proc. of a Conference in May, 1973, volume 2, pages 125–174. Vol. 2 (Dordrecht, Holland,D. Reidel, 1976), 1976. Reprinted in Good Thinking, University of Minnesota Press, 1983, 22–55.
• I.J. Good. Good Thinking. U. Minnesota Press., 1983.
• I.J. Good. Weight of evidence: A brief survey. In J. Bernardo, M. DeGroot, D. Lindley, and A Smith, editors, Bayesian Statistics 2, pages 249–269. North Holland, 1985.
• E. A. Graf. Designing a proficiency model and assiated item models for an 8th-grad mathematics unit on sequences. In Cross Division Math Forum, Princeton, NJ, September 2003.
• I. Guttman. The use of the concpet of a future observation in goodness-of-fit problems. Journal of the Royal Statistical Society, Series B, 29:83–100, 1967.
• A. Habel. Hyperedge Replacement: Grammars and Languages. Springer-Verlag, 1992.
• S. J. Haberman. Log-linear fit for contingency tables — algorithm as51. Applied Statistics, 21:218–225, 1972.
• E.H. Haertel. An application of latent class models to assessment data. Applied Psychological Measurement, 8:333–346, 1984.
• E.H. Haertel. Using restricted latent class models to map the skill structure of achievement test items. Journal of Educational Measurement, 26:301–321, 1989.
• R.K. Hambleton, H. Swaminathan, and H.J. Rogers. Fundamentals of item response theory. Newbury Park, CA: Sage, 1991.
• E. G. Hansen and R. J. Mislevy. Toward a unified validity framework for ensuring access to assessments by individuals with disabilities and english language learners. In Annual meeting of the National Council on Measurement in Education (NCME), 2004.
• E. G. Hansen, R. J. Mislevy, and L. S. Steinberg. Evidence-centered assessment design and individuals with disabilities. In Annual meeting of the National Council on Measurement in Education, 2003.
• S. Hartz, L. Roussos, and W. Stout. The fusion model for cognitive diagnosis: blending theory with practicality. Psychometrika, TBA:TBA, Submitted.
• W. Hastings. Monte carlo sampling methods using markov chains and their applications. Biometrika, 57:97–109, 1970.
• D. Heckerman, E. Horvitz, and B. Middleton. An approximate nonmyopic computation for value of information. IEEE Transaction of Pattern Analysis and Machine Intelligence, 15:292–298, 1993.
• D. Heckerman, D. Gieger, and D. Chikering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197–243, 1995.
• D. Heckerman. Probabilistic Similarity Networks. New York:ACM Press, 1991.
• D. Heckerman. A tutorial on learning with Bayesian networks. In M.I. Jordan, editor, A tutorial on learning with Bayesian networks, pages 301–354. Kluwer Academic Publishers, 1998. Also Technical Report MSR-TR-95-06, Microsoft Research, March, 1995 (revised November, 1996). (PDF)
• Lisa Hemat, Russell Almond, and Jan Lukas. StatShop Documentation. Educational Testing Service, Version 2.0 edition, December 2005. URL is internal to ETS.
• M. Henrion and M. Druzdzel. Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning. In Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, pages 10–20. Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, 1990.
• M. Henrion, J.S. Breese, and E.J. Horvitz. Decision analysis and expert systems. AI Magazine, 12:64–91, 1991.
• R. Hensen and J. Douglas. Test construction for cognitively diagnostic assessment. Draft report, 2003.
• J. Hilden. Genex — an algebraic approach to pedigree proability calculus. Clinical Genetics, 1:319–348, 1970.
• P.W. Holland and H. Wainer. Differential item functioning. Lawrence Erlbaum Associates, 1993.
• P.W. Holland. On the study of differential item performance without irt. In Proceedigns of the 27th annual conference of the Miliatry Testing Association, pages 282–287, 1985.
• S.H. Holtzman. Intelligent Decision Systems. Reading, Massachusetts:Addison-Wesley, 1989.
• E.J. Horvitz, J. Breese, and M. Henrion. Decision theory in expert systems and artificial reasoning, special issue on uncertainty in artificial intelligence. International Journal of Approximate Reasoning, 2:247–302, 1988. Also appears in M. Henrion, J.S. Breese, and E.J. Horvitz [1991] "Decision analysis and expert systems." AI Magazine 12(4), 64-91.
• R.A. Howard and J.E. Matheson. Influence diagrams. In Principles and Applications of Decision Analysis. Strategic Decisions Group, Menlo Park, CA, 1981.
• R.A. Howard. Knowledge maps. Management Science, 35:903–922, 1989.
• T. Hrycej. Gibbs sampling in Bayesian networks. Artificial Intelligence, 46:351–363, 1990.
• S.H. Irvine, P.L. Dann, and J.D. Anderson. Towards a theory of algorithm-determined cognitive test construction. British Journal of Psychology, 81:173–195, 1990.
• T.S. Jaakkola and M.I. Jordan. Improving the mean field approximation via the use of mixture distributions. In Jordan, editor, [1998] Learning in Graphical Models, pages 163–173. Kulwer Academic Publishers, 1998.
• T.S. Jaakkola. Tutorial on variational approximation methods. In M. Opper and D. Saad, editors, Advanced Mean Field Methods: Theory and Practice, pages 129–159. MIT Press., 2001.
• E.T. Jaynes. Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC-4:227–241, 1968.
• H. Jeffreys. Theory of Probability (3rd edn.). Oxford Univeristy Press, 1961.
• F.V. Jensen and F. Jensen. Uncertainty in artificial intelligence:proceedings of the 10th conference. In R.L. de Mantras and D. Poole, editors, Optimal Junction Trees, pages 360–366. Morgan Kaufmann, 1994.
• F.V. Jensen. Junction trees and decomposable hypergraphs. Technical report, JUDEX Research Report, Aalborg, Denmark, 1988.
• F.V. Jensen. An Introduction to Bayesian Networks. Springer-Verlag, 1996.
• M.S. Johnson and S. Sinharay. Calibration of polytomous item families using Bayesian hierarchical modeling. Research Report RR-03-23, ETS, 2003.
• M.S. Johnson and S. Sinharay. Simulation studies applying posterior predictive model checking for assessing fit of the common item response theory models. Research Report RR-03-28, ETS, 2003.
• M.I. Jordan, Z. Ghahramani, T.S. Jaakkola, and L.K. Saul. An introduction to variational methods for graphical models. In Learning in Graphical Models, pages 105–161. Kluwer Academic Publishers., 1998.
• M.I. Jordan. Learning in Graphical Models. Kluwer Academic Publishers, 1998. Reprinted by MIT Press.
• K.G. Joreskog and D. Sorbom. Advances in factor analysis and structural equation models. Abt Books, 1979.
• B.W. Junker and K. Sijtsma. Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25:258–272, 2001.
• B.W. Junker. Conditional association, essential independence and monotone unidimensional item response models. Annals of Statistics, 21:1359–1378, 1993.
• J.B. Kadane, J.M. Dickey, R.L. Winkler, W.S. Smith, and S.C. Peters. Interactive elicitation of opinion for a normal linear model. JASA, 75:845–854, 1980.
• J.B. Kadane. Predictive and structureal methods for eliciting prior distributions. In A. Zellner, editor, Bayesian Analysis and Statistics. North-Holland, 1980.
• C.M. Kadie, D. Hovel, and E. Horvitz. MSBNx: A Component-Centric Toolkit for Modeling and Inference with Bayesian Networks, 2001. Microsoft Research Technical Report MSR-TR-2001-67.
• D. Kahneman, P. Slovic, and A. Tversky. Judgement under uncertainty: Heuristics and biases. Cambridge University Press, 1982.
• R.E. Kalman and R.S. Bucy. New results in linear predicition and filtering theory. Transactions ASME, Series D, J. Basic Eng., 83:95–107, 1961.
• M.T. Kane. An argument-based approach to validity. Psychological Bulletin, 112:527–535, 1992.
• D. Kaplan. Structural Equation Modeling: Foundations and Extensions. Sage Publications, 2000.
• I. R. Katz, D. M. Williamson, H. L. Nadelman, I. Kirsch, R. G. Almond, P. L. Cooper, M. L. Redman, and D. Zapata. Assessing information and communications technology literacy for higher education. In International Association for Educational Assessment, 2004.
• J.H. Kim and J. Pearl. A computational model for causal and diagnostic reasoning in inference systems. In Proceedings of the 8th International Joint conference on Artificial Intelligence, pages 190–193. William Kaufmann, 1983.
• U. Kjæ rulff. Optimal decomposition of probabilistic networks by simulated annealing. Statistics and Computing, 2:19–24, 1992.
• M.F. Klein, M. Birenbaum, S.N. Standiford, and K.K. Tatsuoka. Logical error analysis and construction of tests to diagnose student "bugs" in addition and subtraction of fractions. Research Report 81-6, Computer-based Education Research Laboratory, University of Illinois, 1981.
• Daphne Koller and Uri Learner. Sequential monte carlo methods in practice. In Arnaud Doucet, Nando de Freitas, and Neil Gordon, editors, Sampling in Factored Dynamic Systems, pages 445–464. Springer, 2001.
• D. Koller and A. Pfeffer. Object-oriented Bayesian networks. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 302–313, 1997.
• C.T.A. Kong. Multivariate belief functions and graphical models. Technical Report S-107, Harvard University, Department of Statistics, 1986. Ph.D. thesis.
• C.T.A. Kong. A belief function generalization of gibbs ensembles. Technical Report S-122, Harvard University, Departments of Statistics, 1988. Also University of Chicago, Department of Statistics, Research Report No. 239.
• C.T.A. Kong. Analysis of pedigree data using a combination of peeling and gibbs sampling. In Elaine Kermedes, editor, Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface Interface Foundation of North America, pages 379–386, 1991.
• H.E. Jr. Kyburg. Probability and the Logic of Rational Belief. Wesleyan University Press, 1961.
• K.B. Laskey and S.M. Mahoney. Network engineering for agile belief network models. IEEE Transactions on Knowledge and Data Engineering, 12:481–486, 2000.
• S.L. Lauritzen and D.J. Spiegelhalter. Local computation with probabilities on graphical structures and their application to expert systems (with discussion). Journal of the Royal Statistical Society,Series B, 50:205–247, 1988. Reprinted in Shafer and Pearl[1990].
• S.L. Lauritzen and N. Wermuth. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17:31–57, 1989.
• S.L. Lauritzen. Propagation of probabilities, means, and variances in mixed graphical association models. Journal of the American Statistical Association, 87:1098–1108, 1992.
• S.L. Lauritzen. Graphical Models Oxford Statistical Science Series. Claredon Press, Oxford, England., 1996.
• R. Levy and R.J. Mislevy. Specifying and refining a measurementmodel for a simulation-based assessment. International Journal of Measurement, 4:333–369, 2004.
• Z. Li and B. D'Ambrosio. Efficient inference in Bayes nets as a combinatorial optimization problem. Intl Journal of Approximate Reasoning, 11:55–81, 1994.
• J.S. Liu. Monte Carlo Strategies in Scientific Computing. Springer, 2001.
• F.M. Lord. Applications of Item Response Theory to Practical Testing Problems. Lawerence Earlbaum Associatiates, 1980.
• T.A. Louis. Finding the observed information matrix when using the em algorithm. Journal of the Royal Statistical Society, Series B, 44:226–233, 1982.
• D. Madigan and R.G. Almond. Test selection strategies for belief networks. In D. Fisher and H.J. Lenz, editors, Learning from Data: AI and Statistics V, pages 89–98. Springer-Verlag, 1995.
• D. Madigan and A.E. Raftery. Model selection and accounting for model uncertainty in graphical models using occam's window. JASA, 89:1535–1546, 1994.
• D. Madigan and J.C. York. Bayesian graphical models. Technical Report 259, University of Washington, Department of Statistics, 1993.
• D. Madigan, A. E. Raftery, J. C. York, J. M. Bradshaw, and R. G. Almond. Strategies for graphical model selection. In P. Cheeseman and W. Oldford, editors, Selecting Models from Data: Artificial Intelligence and Statistics IV, pages 91–100. Springer-Verlag, 1994.
• D. Madigan, J. Gavrin, and A.E. Raftery. Enhancing the predictive performance of Bayesian graphical models.enhancing the predictive performance of Bayesian graphical models. Communications in Statistics: Theory and Methods, 24:2271–2292, 1995.
• D. Madigan, C. Raftery, A.E.and Volinsky, and J. Hoeting. Bayesian model averaging. In Proceedings of the AAI Workshop on Integrating Multiple Learned Models, 1996.
• D. Madigan, K. Mosurski, and R.G. Almond. Graphical explanation in belief networks. Journal of Computational Graphics and Statistics, 6(2):160–181, 1997.
• D. Maier. The Theory of Relational Databases. Computer Science Press, Rockville, Maryland., 1983.
• J.D. Martin and K. VanLehn. Cognitively diagnostic assessment. In P. Nichols, S. Chipman, and R. Brennan, editors, A Bayesian approach to cognitive assessment, pages 141–165. Erlbaum, 141–165, 1995.
• J.E. Matheson. Using influence diagrams to value information and control. In Robert M. Oliver and Smith James Q., editors, Influence Diagrams, Belief Nets and Decision Analysis. John Wiley & Sons, 1990.
• N. Matsuda and K. VanLehn. Decision theoretic instructional planner for intelligent tutoring systems. In In Proceedings for Workshop on Modeling Human Teaching Tactics and Strategies, pages 72–83. ITS 2000, 2000.
• P. McCullagh and J.A. Nelder. Generalized Linear Models. (2nd Edition). Chapman and Hall, 1989.
• M. Meil u  a and M.I. Jordan. Learning with mixtures of trees. Journal of Machine Learning Research, 1:1–48, 2000.
• M.D. Merrill, Z. Li, and M.K. Jones. Instructional transaction shells: Responsibilities, methods, and parameters. Educational Technology, 32 (2):5–26, 1992.
• S. Messick. Validity. In R.L. Linn, editor, Educational measurement (3rd Ed.), pages 13–103. American Council on Education/Macmillan, 1989.
• S. Messick. The interplay of evidence and consequences in the validation of performance assessments. Educational Researcher, 23(2):13–23, 1994.
• N. Metropolis and S. Ulam. The monte carlo method. Journal of the American Statistical Association, 44:335–341, 1949.
• P. Miller. Attending: Critiquing a physician's management plan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:449–461, 1983.
• R.J. Mislevy and D Bock. Bilog 3: Item Analysis and Test Scoring with Binary Logistic Models. Scientific Software.
• R.J. Mislevy and D.H. Gitomer. The role of probability based inference in an intelligent tutoring system. User-Modeling and User-Adapted Interaction, 5:253–282, 1996.
• R.J. Mislevy, K.M. Sheehan, and M.S. Wingersky. How to equate tests with little or no data. Journal of Educational Measurement, 30:55–78, 1993.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. Task design, student modeling, and evidentiary reasoning in complex educational assessments. In Section on Bayesian Statistical Science at the Annual Meeting of the American Statistical Association, 1997.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. A note on knowledge-based model construction in educational assessment. CSE Technical Report 480, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 1998. Numerical example for testing Ergo.
• R.J. Mislevy, L.S. Steinberg, and R. G. Almond. Evidence-centered assessment design. In Annual Conference of the National Center for Research on Evaluation, Standards, Student Testing (CRESST), 1998.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. Evidentiary considerations in performance assessment. In Annual Meeting of the National Council on Measurement in Education, 1999.
• R.J. Mislevy, L.S. Steinberg, F.J. Breyer, R.G. Almond, and L. Johnson. A cognitive task analysis, with implications for designing a simulation-based assessment system. Computers and Human Behavior, 15:29–42, 1999.
• Robert J. Mislevy, Russell G. Almond, Duanli Yan, and Linda S. Steinberg. Bayes nets in educational assessment: Where the numbers come from. In K. B. Laskey and H. Prade, editors, Uncertainty in Artificial Intelligence '99, pages 437–446. Morgan-Kaufmann, 1999.
• R.J. Mislevy, L.S. Steinberg, F.J. Breyer, R.G. Almond, and L. Johnson. Making sense of data from complex assessments. CSE Technical Report 538, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2001.
• Robert J. Mislevy, Russell G. Almond, Lou DiBello, Frank Jenkins, Linda S. Steinberg, Duanli Yan, and Deniz Senturk. Modeling conditional probabilities in complex educational assessments. CSE Technical Report 580, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2002. Numerical example for testing Ergo.
• Robert J. Mislevy, Russell G. Almond, and L.S. Steinberg. Design and analysis in a task-based language assessment. Language Testing, 19(4):477–496, 2002.
• Robert J. Mislevy, Linda S. Steinberg, and Russell G. Almond. On the roles of task model variables in assessment design. In S. Irvine and P. Kyllonen, editors, Generating items for cognitive tests: Theory and practice, pages 97–128. Erlbaum, 2002.
• R. J. Mislevy, L.S. Steinberg, R.G. Almond, G. Haertel, and W. Penuel. Leverage points for improving educational assessment. In B. Means and G. Haertel, editors, Evaluating the Effects of Technology in Education, pages 149–180. Earlbaum, 2003.
• Robert J. Mislevy, Linda S. Steinberg, and Russell G. Almond. On the structure of educational assessment (with discussion). Measurement: Interdisciplinary Research and Perspective, 1(1):3–62, 2003.
• R.J. Mislevy, R.G. Almond, and J. Lukas. A brief introduction to evidence-centered design. CSE Technical Report 632, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2004. Also ETS Research Report RR-03-32.
• R.J. Mislevy. Evidence and inference in educational assessment. Psychometrika, 12:341–369, 1994.
• R.J. Mislevy. Cognitively diagnostic assessment. In P. Nichols, S. Chipman, and R. Brennan, editors, Probability-based inference in cognitive diagnosis, pages 43–71. Erlbaum, 1995.
• R.J. Mislevy. Test theory and language learning in assessment. Language Testing, 12:341–369, 1995.
• R.J. Mislevy. Bayes modal estimation of item parameters, 1996.
• P. Mohan, J. Greer, and G. I. McCalla. Instructional planning with learning objects. In Eighteenth International Joint Conference On Artificial Intelligence Workshop on Knowledge Representation and Automated Reasoning for E-Learning Systems (KRR-5), 2003.
• M.G. Morgan and M. Henrion. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press., 1990.
• J. Moussouris. Gibbs and markov random systems with constraints. Journal of Statistical Physics, 10:11–33, 1974.
• C. Mundhenk, M.and Lusena, J. Goldsmith, and E. Allender. The complexity of finite-horizon markov decision process problems. Journal of the ACM, 47(4):681–720, 2000.
• Kevin Murphy and Stuart Russell. Rao-blackwellised particle filtering for dynamic Bayesian networks. In Arnaud Doucet, Nando de Freitas, and Neil Gordon, editors, Sequential Monte Carlo Methods in Practice, pages 499–515. Springer, 2001.
• A.H. Murphy and R.L. Winkler. Probability forecasting in meteorology. JASA, 79:489–500, 1984.
• K.P. Murphy, Y. Weiss, and M.I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In K.B. Laskey and H. Prade, editors, Uncertainty in Artificial Intelligence, Proceedings of the Fifteenth Conference (UAI 99), pages 467–475. Morgan Kaufmann, 1999.
• R.M. Neal. Slice sampling (with discussion). Annals of Statistics, 31:705–767, 2003.
• R.E. Neapolitan. Probabilistic Reasoning in Expert Systems:Theory and Algorithms. Wiley, 1990.
• R.E. Neapolitan. Learning Bayesian Networks. Prentice Hall, 2004.
• J. Neyman and E.L. Scott. Consistent estimators based on partially consistent observations. Econometrika, 16:1–32, 1948.
• K.C. Ng and B. Abramson. Consensus in a multi-expert system. In Proceedings of the 1990 ACM Eighteenth Annual Computer Science Conference, pages 351–357, 1990.
• A.E. Nicholson and N. Jitnah. Using mutual information to determine relevance in Bayesian networks. In Pacific Rim International Conference on Artificial Intelligence, pages 399–410, 1998.
• A. O'Hagan. On posterior joint and marginal modes. Biometrika, 63:329–333, 1976.
• R.M. Oliver and J.Q. Smith. Influence Diagrams,Belief Nets and Decision Analysis. John Wiley and Sons, 1990.
• R.J. Patz and B.W. Junker. Applications and extensions of mcmc in irt: Multiple item types, missing data, and rated responses. Journal of Educational and Behavioral Statistics, 24:342–366, 1999.
• R.J. Patz and B.W. Junker. A straight forward approach to markov chain monte carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24:146–178, 1999.
• D. Peachey and G. I. McCalla. Using planning techniques in intelligent tutoring systems. International Journal of Man Machine Studies, 24(1):77–98, 1986.
• J. Pearl. Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29:241–288, 1982.
• J. Rissanen. Stochastic complexity (with discussion). JRSS B, 49:223–265, 1987.
• D.J. Rose, R.E. Tarjan, and G.S. Lueker. Algorithmic aspects of vertex elimination on graphs. Siam J. Comput., 5:266–283, 1976.
• S.M. Ross. A First Course in Probability. Macmillan, 1988.
• S.M. Ross. Probability Models. Academic Press, 1989.
• J. Rost. Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14:271–282, 1990.
• D.B. Rubin. Multiple imputation in sample surveys: A pheonomenological bayesian approach to nonresponse. In Proceedings of the Survey Research Methods Section, pages 20–44. American Statistical Association, 1978.
• D.B. Rubin. Bayesian justifiable and relevant frequency calculations for the applied statistician. Annals of Statistics, 12:1151–1172, 1984.
• F. Samejima. Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph No. 17, 34(4):(Part 2), 1969.
• L.J. Savage. The Foundations of Statistics (Second Edition). Dover, 1972. (First edition, 1954, Wiley.).
• D.A. Schum. The evidential foundations of probabilistic reasoning. Wiley, 1994.
• G. Schwarz. Estimating the dimension of a model. Annals of Statistics, 6:461–464, 1978.
• R.D. Shachter. Evaluating influence diagrams. Operations Research, 34:871–82, 1986. Reprinted in Shafer and Pearl[1990].
• G. Shafer and J. Pearl. Readings in Uncertain Reasoning. Morgan Kaufmann, 1990.
• G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, 1976.
• G. Shafer. Belief functions and parametric models. Journal of the Royal Statistical Society, Series B, 44:322–352, 1982.
• G. Shafer. Perspectives on the theory and practice of belief fucntions. International Journal of Approximate Reasoning, 4:323–362, 1990.
• G. Shafer. The unity and diversity of probability (with discussion). Statistical Sciences, 5:435–462, 1990.
• G. Shafer. The Art of Causal Conjecture. MIT Press, 1996.
• Idres Shah. Tales of the Dervishes. Dutton, 1970.
• P.P. Shenoy and G. Shafer. Axioms for probability and belief-function propagation. In Uncertainty in Artificial Intelligence 4, pages 169–198, 1990. Reprinted in Shafer and Pearl[1990].
• P.P. Shenoy. Valuation-based systems for discrete optimization. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pages 334–343, 1990.
• P.P Shenoy. A fusion algorithm for solving bayesian decision problems. In Uncertainty in Artificial Intelligence, Proceedings of the Seventh Conference, pages 361–369, 1991.
• V. J. Shute and J. Psotka. Intelligent tutoring systems: Past, present, and future. In D. Jonassen, editor, Handbook of Research for Educational Communications and Technology, pages 570–600. Macmillan, 1996.
• V. J. Shute, E. A. Graf, and E. G. Hansen. Designing adaptive, diagnostic math assessments for individuals with and without visual disabilities. In L. M. Pytlikzillig, R. H. Bruning, and M. Bodvarsson, editors, Technology-based education; bringing researchers and practitioners together, pages 169–202. Information Age Publishing, Greenwich, CT, 2005.
• V.J Shute. Under the hood of adaptive e-learning: Diagnostic assessment, student modeling, and selection rules. In American Educational Research Association (AERA), 2003.
• V. J. Shute. Towards automating ecd-based diagnostic assessments. Technology, Instruction, Cognition, and Learning, 2(1-2):1–18, 2004.
• Sandip Sinharay, Russell G. Almond, and Duanli Yan. Assessing fit of models with discrete proficiency variables in educational assessment. Research Report RR-04-07, Educational Testing Service, 2004.
• S. Sinharay. Model diagnostics for bayesian networks. Research Report RR-04-17, ETS, 2004.
• S. Sinharay. Assessing item fit for bayesian networks. Journal of Educational Measurement, page To appear, in press.
• P. Slovic, B. Fischhoff, and S. Lichtenstein. Fact and fears: Understanding perceived risk. In R. C. Schwing and Jr. W.A. Alber, editors, Societal Risk Assessment How Safe is Safe Enough? Proceeding of the General Motors Symposium on Societal Risk Assessment. Pelenum Press, 1979.
• A. Smith and G. Roberts. Bayesian computation via the gibbs sampler and related markov chain monte carlo methods. Journal of the Royal Statistical Society (Series B), 55:To appear, 1993.
• D.J. Spiegelhalter and R. Knill-Jones. Statistical and knowledge-based approaches to clinical decision support systems, with an application in gastroenterology. Journal of the Royal Statistical Society, (Series A), 147:35–77, 1984.
• D.J. Spiegelhalter and S.L. Lauritzen. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579–605, 1990.
• D.J. Spiegelhalter, A.P. Dawid, S.L. Lauritzen, and R.G. Cowell. Bayesian analysis in expert systems. Statistical Science, 8:219–283, 1993.
• D.J. Spiegelhalter, A. Thomas, N.G. Best, and W.R. Gilks. BUGS: Bayesian inference Using Gibbs Sampling, Version 0.50. MRC Biostatistics Unit, Cambridge, 1995.
• D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64:583–639, 2002.
• P. Spirtes, C. Meek, and T.S. Richardson. A polynomial-time algorithm for determining dag equivalence in the presence of latent variables and selection bias. In D. Madigan and P. Smythe, editors, Preliminary papers of the Sixth International Workshop on AI and Statistics, pages 489–501, 1987.
• S. Srinivas and J. Breese. Ideal: A software package for analysis of influence diagrams. In Proceedings of the Sixth Uncertainty Conference in AI, 1990.
• S. Srinivas. A generalization of the noisy-or model, the generalized noisy or-gate. In D. Heckerman and A. Mamdani, editors, Uncertainty in Artificial Intelligence '93, pages 208–215. Morgan-Kaufmann, 1993.
• L.S. Steinberg, R.G. Almond, A.B. Baird, C. Cahallan, H. Chernick, L.V. Dibello, A.C.H. Kindfield, R.J. Mislevy, D. Senturk, and D. Yan. Introduction to the biomass project: An illustration of evidence-centered assessment design and delivery capability. CSE Report 609, National Center for Research on Evaluation, Standards, and Student Testing (CRESST)., 2003.
• Linda S. Steinberg, Robert J. Mislevy, and Russell G. Almond. Portal assessment design system for educational testing. U.S. Patent application, Pending. Attorney Docket No. 246400.0159, Wilmer, Culter and Pickering.
• S.M. Stiglar. The History of Statistics: The Measurement of Uncertainty before 1900. Harvard University Press, 1986.
• W. Stout. A theory-based nonparametric approach for assessing latent trait multidimensionality in psychological testing. Psychometrika, 52:589–617, 1987.
• D.J Strauss. Hammersley-clifford theorem. In Samuel Kotz and Norman L. Johnson, editors, Encyclopedia of Statistical Science, volume 5, pages 570–572. Wiley, 1983.
• H. Suermondt. Explanation in Bayesian Belief Networks. PhD thesis, Departments of Computer Science and Medicine, Stanford University, 1992.
• P. Suppes, M. Jerman, and D. Brian. Computer assisted instruction: Stanford's 1965-66 arithmetic program. Academic Press, 1968.
• Noetic Systems. ERGO, 1991.
• M. Takikawa, B. D'Ambrosia, and E. Wright. Real-time inference with large-scale temporal bayes nets. In Jack Breese and Daphne Koller, editors, Proceedings fo the 18th UAI Conference. Morgan Kaufmann, 2002.
• R.E. Tarjan and M. Yannakakis. Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. Siam J. Comput., 13:566–579, 1984.
• K.K. Tatsuoka. Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20:345–354, 1983.
• D. Thissen and H. Wainer. Test Scoring. Lawrence Erlbaum Associates, 2001.
• H.M. Thoma and C. Goodall. Graphical models and their representation. In E.M. Keramidas, editor, Computing Science and Statistics: Proceedings of the 23st Symposium on the Interface, pages 30–37, 1991.
• A. Thomas, D. J. Spiegelhalter, and W. R. Gilks. Bugs: A program to perform bayesian inference using gibbs sampling. In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 4., pages 837–842. Clarendon Press, 1992.
• E Thompson. Probabilities on complex pedigrees; the gibbs sampler approach. In Elaine Kermedes, editor, Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, pages 371–378. Interface Foundation of North America, 1991.
• J. Vassileva and B. Wasson. Instructional planning approaches: from tutoring towards free learning. In Proceedings of Euro-AIED'96, pages 1–8, 1996.
• M. von Davier and A.A. von Davier. A micro-survey of models for individual change. Draft Technical Report, 2005.
• Matthias von Davier. A general diagnostic model applied to language testing data. Research Report RR-05-16, ETS, Princeton, NJ, 2005.
• L. Vygotsky. Mind in society: The development of higher mental processes. Harvard University Press, 1978.
• H. Wainer and G.L. Kiely. Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational Measurement, 24:185–201, 1987.
• H. Wainer, N.J. Dorans, R. Flaugher, B.F. Green, R.J. Mislevy, L. Steinberg, and D. Thissen. Computerized adaptive testing: A primer (second edition). Lawrence Erlbaum Associates, 2001.
• P. Walley. Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, 1991.
• W. Weaver. Probability, rarity, interest, and surprise. Scientific Monthly, 67:390–392, 1948.
• Y. Weiss. Correctness of local probability propagation in graphical models with loops. Neural Computation, 12:1–41, 2000.
• N. Wermuth and S.L. Lauritzen. On substantive research hypotheses, conditional independence graphs and graphical chain models. Journal of the Royal Statistical Society, Series B, 52:21–50, 1990.
• J. Whittaker. Graphical Models in Applied Multivariate Statistics. Wiley, 1990.
• David M. Williamson, Robert J. Mislevy, and Russell G. Almond. Model criticism of Bayesian networks with latent variables. In C. Boutilier and M. Goldszmidt, editors, Uncertainty in Artificial Intelligence 16, pages 634–643. Morgan Kaufmann, 2000.
• David M. Williamson, Robert J. Mislevy, and Russell G. Almond. Evidence-centered design for certification and licensure. CLEAR Exam Review, 14:14–18, 2004.
• D.M. Williamson. Utility of model criticism indices for Bayesian inference networks in cognitive assessment. PhD thesis, Fordham University, 2000.
• S. Wright. Correlation and causation. Journal of Agricultural Research, 20:557–85, 1921.
• S. Wright. The method of path coefficients. Annals of Mathematical Statistics, 5:161–215, 1934.
• Duanli Yan and Russell Almond. Using ROC curves to improve evidence for diagnostic scoring. In Third Annual Bayesian Application Workshop at the 2005 Uncertainty in Artificial Intelligence Application Workshop, 2005.
• Duanli Yan, Robert J. Mislevy, and Russell G. Almond. Design and analysis in a cognitive assessment. Research Report RR-03-32, Educational Testing Service, 2003.
• Duanli Yan, Russell G. Almond, and Robert J. Mislevy. Comparison of two models for cognitive diagnosis. Research Report RR-04-02, Educational Testing Service, 2004.
• Duanli Yan, Russell Almond, and Lisa Hemat. Bayesian network model for the ict literacy assessment. Research Report Pending, Educational Testing Service, Under Review. Under Review.
• M. Yannakakis. Computing the minimum fill-in is np-complete. Siam J. Alg. Disc. Meth., 2:77–79, 1981.
• F. Ye, R.G. Almond, R.J. Mislevy, and D. Yan. Sensitivity to prior distributions in calibration of a Bayesian network. In Annual meeting of the National Council on Measurement in Education, 2004.
• J.S. Yedidia, W.T. Freeman, and Y. Weiss. Generalized belief propagation. In NIPS, pages 689–695, 2000.
• W.M. Yen. Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30:187–213, 1993.
• J. York. Use of the gibbs sampler in expert systems. Artificial Intelligence, 56:115–130, 1992.
• L. Zhang. Studies on finding hypertree covers of hypergraphs. Working Paper 198, School of Business, University of Kansas, 1988.

This bibliography is a work in progress. I would be grateful for any suggested additions, updates or corrections. Special thanks to Juliana Babula at ETS who did much of the initial data entry.