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Bibliography

Agrawal, R., Imilinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings ACM SIGMOD, pages 207–216. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. (1995). Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, Cambridge, MA. AAAI/MIT Press. Andersson, S., Madigan, D., and Perlman, M. (1996). An alternative Markov property for chain graphs. In Jensen, F. and Horvitz, E., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 40–48. Morgan Kaufmann. Andersson, S., Madigan, D., and Perlman, M. (1997a). A characterization of markov equivalence classes for acyclic digraphs. Annals of Statistics, 25:505–541. Andersson, S., Madigan, D., and Perlman, M. (1997b). On the markov equivalence of chain graphs, undirected graphs, and acyclic digraphs. Scandinavian Journal of Statistics, 24:81–102. Andersson, S., Madigan, D., and Perlman, M. (2001). Alternative Markov properties for chain graphs. Scandinavian Journal of Statistics, 28:33–85. Andersson, S., Madigan, D., Perlman, M., and Triggs, C. (1995). On the relation between conditional independence models determined by ?nite distributive lattices and by directed acyclic graphs. Journal of Statistical Planning and Inference, 48:25–46. Andersson, S., Madigan, D., Perlman, M., and Triggs, C. (1997c). A characterization of lattice conditional independence models. Annals of Mathematics and Arti?cial Intelligence, 21:27–50. Andersson, S. and Perlman, M. (1993). Lattice models for conditional independence in a multivariate normal distribution. Annals of Statistics, 21:1318–1358. Angelopoulos, N. and Cussens, J. (2001). Markov chain monte carlo using tree-based priors on model structure. In Breese, J. and Koller, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 16–23. Morgan Kaufmann. Asmussen, S. and Edwards, D. (1983). Collapsability and response variables in contingency tables. Biometrika, 70(3):567–578.

132

BIBLIOGRAPHY

Beeri, C., Fagin, R., Maier, D., Mendelzon, A., Ullman, J., and Yannakakis, M. (1981). Properties of acyclic database schemes. In Proc. of the Thirteenth Annual ACM Symposium on Theory of Computation, pages 355–362. Beinlich, I., Suermondt, H., Chavez, R., and Cooper, G. (1989). The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proc. of the Second European Conference on Arti?cial Intelligence in Medicine, pages 247– 256. Springer-Verlag. Boncz, P. and Kersten, M. (1995). Monet: An Impressionist Sketch of an Advanced Database System. In Proceedings Basque International Workshop on Information Technology, San Sebastian, Spain. Bouckaert, R. (1992). Optimizing causal orderings for generating dags from data. In Dubois, D., Wellman, M., D’Ambrosio, B., and Smets, P., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 9–16. Morgan Kaufmann. Bouckaert, R. (1995). Bayesian Belief Networks: from Construction to Inference. PhD thesis, University of Utrecht. Breese, J., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative ?ltering. In Proc. of the Conf. on Uncertainty in Arti?cial Intelligence. Morgan Kaufmann. Brooks, S. (1998). Markov chain monte carlo method and its application. The Statistician, 47:69–100. Buntine, W. (1991). Theory re?nement on Bayesian networks. In B. D’Ambrosio, P. S. and Bonissone, P., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 52– 60. Morgan Kaufmann. Buntine, W. (1996a). Graphical models for discovering knowledge. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., editors, Advances in Knowledge Discovery and Data Mining, pages 59–82. AAAI Press. Buntine, W. (1996b). A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8(2):195–210. Carlin, B. and Chib, S. (1995). Bayesian model choice via Markov chain monte carlo methods. Journal of the Royal Statistical Society B, 57(3):473–484. Castelo, R., Feelders, A., and Siebes, A. (2001). Mambo: Discovering association rules based on conditional independencies. In Hoffmann, F., Hand, D., Adams, N., Fisher, D., and Guimar? es, G., editors, Proceedings 4th Symposium on Intelligent Data Analysis, a volume 2189 of Lecture Notes in Computer Science, pages 289–298. Springer. Castelo, R. and Koˇ ka, T. (2002). Towards an inclusion driven learning of Bayesian netc works. Technical Report UU-CS-2002-05, Institute for Computing and Information Sciences, University of Utrecht, The Netherlands. Submitted to the Journal of Machine Learning Research.

BIBLIOGRAPHY

133

Castelo, R. and Siebes, A. (2000). Priors on network structures. biasing the search for Bayesian networks. International Journal of Approximate Reasoning, 24(1):39–57. Castelo, R. and Siebes, A. (2001). A characterization of moral transitive acyclic directed graph Markov models as labeled trees. Journal of Statistical Planning and Inference, to appear. Castelo, R. and Wormald, N. (2001). Enumeration of P4 -free chordal graphs. Technical Report UU-CS-2001-12, Institute for Computing and Information Sciences, University of Utrecht, The Netherlands. Submitted to the Journal of Graphs and Combinatorics. Castillo, E., Ferrandiz, J., and Sanmartin, P. (1998). Marginalizing in undirected graph and hypergraph models. In Cooper, G. and Moral, S., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 69–78. Morgan Kaufmann. Castillo, E., Hadi, A., and Solares, C. (1997). Learning and updating of uncertainty in dirichlet models. Machine Learning, 26:43–63. Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. American Statistician, 49(4):327–335. Chib, S. and Jeliazkov, I. (2001). Marginal likelihood from the metropolis-hastings output. Journal of the American Statistical Association, 96(453):270–281. Chickering, D. (1995). A transformational characterization of equivalent Bayesian networks. In Besnard, P. and Hanks, S., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 87–98. Morgan Kaufmann. Chickering, D. (1996a). Learning Bayesian networks is NP-complete. In Fisher, D. and Lenz, H.-J., editors, Learning from Data: Arti?cial Intelligence and Statistics V, pages 121– 130. Springer-Verlag. Chickering, D. (1996b). Learning equivalence classes of Bayesian network structures. In Horvitz, E. and Jensen, F., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 150–157. Morgan Kaufmann. Chickering, D. (2001). Learning equivalence classes of Bayesian-network structures. Technical report, Microsoft Research. Chickering, D. (2002). Personal Communication. Chickering, D., Geiger, D., and Heckerman, D. (1995). Learning Bayesian networks: Search methods and experimental results. In Proc. of the Int’l. Workshop on Arti?cial Intelligence and Statistics, pages 112–128. Chung, K. (1967). Markov Chains with Stationary Transition Probabilities (2nd ed). SpringerVerlag. Cohen, E., Datar, M., Funjiwara, S., Gionis, A., Indyk, P., Motwani, R., Ullman, J., and Yang, C. (2001). Finding interesting associations without support pruning. IEEE Transactions on Knowledge and Data Engineering, 13(1):64–78.

134

BIBLIOGRAPHY

Cooper, G. and Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–405. Cowell, R., Dawid, A., Lauritzen, S., and Spiegelhalter, D. (1999). Probabilistic Networks and Expert Systems. Springer-Verlag, New York. Cox, D. and Wermuth, N. (1996). Multivariate Dependencies – Models, Analysis and Interpretations. Chapman & Hall, London. D. Geiger, A. P. and Pearl, J. (1993). Learning simple causal structures. International Journal of Intelligent Systems, 8:231–247. Davey, B. and Priestley, H. (1990). Introduction to Lattices and Order. Cambridge University Press, Cambridge. Dawid, A. (1979). Conditional independence in statistical theory (with discussion). Journal of the Royal Statistical Society B, 41(1):1–31. Dawid, A. and Lauritzen, S. (1993). Hyper-Markov laws in the statistical analysis of decomposable graphical models. Annals of Statistics, 21(3):1272–1317. de Campos, L. and Huete, J. (1992). Ef?cient algorithms for learning simple belief networks. Technical report, DECSAI, Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Arti?cial. de Campos, L. and Huete, J. (1997). Algorithms for learning decomposable models and chordal graphs. In Geiger, D. and Shenoy, P., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 46–53. Morgan Kaufmann. DeGroot, M. H. (1970). Optimal Statistical Decisions. McGraw-Hill. Dellaportas, P. and Forster, J. (1999). Markov chain monte carlo model determination for hierarchical and graphical log-linear models. Biometrika, 86(3):615–633. Deshpande, A., Garofalakis, M., and Jordan, M. (2001). Ef?cient stepwise selection in decomposable models. In Breese, J. and Koller, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence. Morgan Kaufmann. Draper, D. (1995). Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society B, 57:45–97. Edwards, D. and Havr? nek, T. (1985). A fast procedure for model search in multidimena sional contingency tables. Biometrika, 72(2):339–351. ? Etxeberria, R., Larranaga, P., and Pikaza, J. (1997). Analysis of the behaviour of the genetic algorithms when searching Bayesian networks from data. Pattern Recognition Letters, 18(11–13):1269–1273. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery: An overview. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., editors, Advances in Knowledge Discovery and Data Mining, pages 1–34. AAAI Press.

BIBLIOGRAPHY

135

Friedman, J. (1997). Data mining and statistics: What’s the connection? In Proc. of the 29th. Symposium on the Interface: Computing Science and Statistics, Houston, Texas. Friedman, N. and Koller, D. (2000). Being Bayesian about network structure. In Boutilier, C. and Goldszmidt, M., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence. Morgan Kaufmann. Frydenberg, M. (1990a). The chain graph Markov property. Scandinavian Journal of Statistics, 17:333–353. Frydenberg, M. (1990b). Marginalization and collapsability in graphical interaction models. Annals of Statistics, 18(2):790–805. Frydenberg, M. and Lauritzen, S. (1989). Decomposition of maximum likelihood in mixed interaction models. Biometrika, 76(3):539–555. Geiger, D. and Heckerman, D. (1995). A characterization of the dirichlet distribution with application to learning Bayesian networks. In Besnard, P. and Hanks, S., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 999–999. Morgan Kaufmann. Gillispie, S. and Perlman, M. (2001). Enumerating Markov equivalence classes of acyclic digraph models. In Breese, J. and Koller, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 171–177. Morgan Kaufmann. Giudici, P. and Castelo, R. (2001a). Association models for Web Mining. Journal of Data Mining and Knowledge Discovery, 24(1):39–57. Giudici, P. and Castelo, R. (2001b). Improving Markov chain monte carlo model search for data mining. Machine Learning, 50(1/2). Giudici, P. and Green, P. (1999). Decomposable graphical gaussian model determination. Biometrika, 86(4):785–801. Giudici, P. and Passerone, G. (2001). Data mining of association structures to model consumer behaviour. Journal of Computational Statistics and Data Analysis, to appear. Glymour, C. (1995). Available technology for discovering causal models, building bayes nets, and selecting predictors: The tetrad ii program. In Fayyad, U. and Uthurusamy, R., editors, Proc. of the Int’l. Conf. on Knowledge Discovery and Data Mining, pages 130– 135, Montreal, Quebec. Golumbic, M. (1978). Trivially perfect graphs. Discrete Mathematics, 24:105–107. Goodman, R., Smyth, P., Higgins, C., and Miller, J. (1992). Rule-based neural networks for classi?cation and probabity estimation. Neural Computation, 4(6):781–804. Gr¨ tzer, G. (1978). General Lattice Theory. Birkh¨ user Verlag, Basel. a a Green, P. (1995). Reversible jump Markov chain monte carlo computation and Bayesian model determination. Biometrika, 82(4):711–732. Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco.

136

BIBLIOGRAPHY

Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining. MIT Press, Cambridge, MA. Harary, F., Kabell, J., and McMorris, F. (1992). Subtree acyclic digraphs. Ars Combinatoria, 34:93–95. Harary, F. and Palmer, E. (1973). Graphical Enumeration. Academic Press, New York. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning. Springer. Hastings, W. (1970). Monte carlo sampling methods using Markov chains and their applications. Biometrika, 57(1):97–109. Havr? nek, T. (1984). A procedure for model search in multidimensional contingency a tables. Biometrics, 40:95–100. Hearne, T. and Wagner, C. (1973). Minimal covers of ?nite sets. Discrete Mathematics, 5:247–251. Heckerman, D. (1996). Bayesian networks for discovering knowledge. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., editors, Advances in Knowledge Discovery and Data Mining, pages 273–305. AAAI Press. Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., and Kadie, C. (2000). Dependency networks for inference, collaborative ?ltering and data visualization. Journal of Machine Learning Research, 1:49–75. Heckerman, D., Geiger, D., and Chickering, D. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:194–243. Herskovits, E. (1991). Computer-Based Probabilistic Network Construction. PhD thesis, Medical Information Sciences, Stanford University. Jensen, F. and Jensen, F. (1994). Optimal junction trees. In de Mantaras, R. L. and Poole, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 360–366. Morgan Kaufmann. Kass, R. and Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90(430):773–795. Kiiveri, H., Speed, T., and Carlin, J. (1984). Recursive causal models. J. Austral. Math. Soc. Ser. A, 36:30–52. Koˇ ka, T. (2001). Graphical Models: learning and applications. PhD thesis, Faculty of Inforc matics and Statistics, University of Prague. Koˇ ka, T., Bouckaert, R., and Studeny, M. (2001). On characterizing inclusion of Bayesian c ? networks. In Breese, J. and Koller, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 261–268. Morgan Kaufmann.

BIBLIOGRAPHY

137

Koˇ ka, T. and Castelo, R. (2001). Improved learning of Bayesian networks. In Breese, J. c and Koller, D., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 269–276. Morgan Kaufmann. Kullback, S. and Leibler, R. (1951). On information and suf?ciency. Annals of Mathematical Statistics, 22:79–86. ? Larranaga, P., Kuijpers, C., Murga, R., and Yurramendi, Y. (1996a). Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics, 26(4):487–493. ? Larranaga, P., Poza, M., Yurramendi, Y., Murga, R., and Kuijpers, C. (1996b). Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9):912–926. Lauritzen, S. (1996). Graphical Models. Oxford University Press, Oxford. Lauritzen, S., Dawid, A., Larsen, B., and Leimer, H. (1990). Independence properties of directed Markov ?elds. Networks, 20:491–505. Lauritzen, S. and Wermuth, N. (1989). Graphical models for association between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17:31– 57. Leimer, H. (1989). Triangulated graphs with marked vertices. Annals of Discrete Mathematics, 41:311–324. Madigan, D., Andersson, S., Perlman, M., and Volinsky, C. (1996). Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. Communications in Statistics (theory and methods), 25(11):2493–2512. Madigan, D. and Raftery, A. (1994). Model selection and accounting for model uncertainty in graphical models using occam’s window. Journal of the American Statistical Association, 89(428):1535–1546. Madigan, D. and York, J. (1995). Bayesian graphical models for discrete data. International Statistical Review, 63:215–232. Meek, C. (1997). Graphical models, selecting causal and statistical models. PhD thesis, Carnegie Mellon University. Melancon, G., Dutour, I., and Bousquet-Melou, M. (2000). Random generation of dags ? for graphs drawing. Technical report, Centrum voor Wiskunde en Informatica. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087– 1092. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, California.

138

BIBLIOGRAPHY

Pearl, J. and Paz, A. (1987). Graphoids: A graph-based logic for reasning about relevancy relations. In Boulay, B. D., editor, Advances in Arti?cial Intelligence-II. North-Holland. Pearl, J. and Verma, T. (1987). The logic of representing dependencies by directed graphs. In Proc. of the Conf. of the American Association of Arti?cial Intelligence, pages 374–379. ¨ Sanguesa, R. and Cort? s, U. (1997). Learning causal networks from data: a survey and a e new algorithm for recovering possibilistic causal networks. AI Communications, 10:31– 61. Silverstein, C., Brin, S., and Motwani, R. (1998). Beyond market baskets: Generalizing associations rules to dependence rules. In Data Mining and Knowledge Discovery. Singh, M. and Valtorta, M. (1993). An algorithm for the construction of Bayesian network structures from data. In Heckerman, D. and Mamdani, A., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 259–265. Morgan Kaufmann. Smith, A. and Roberts, G. (1993). Bayesian computation via the gibbs sampler and related Markov chain monte carlo methods. Journal of the Royal Statistical Society B, 55(1):3–23. Smyth, P. (2001). Data mining at the interface of computer science and statistics (invited chapter). In “Data Mining for Scienti?c and Engineering Applications”. Spiegelhalter, D. and Lauritzen, S. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579–605. Spiegelhalter, D., Thomas, A., and Best, N. (1996). Computation on Bayesian graphical models. In Bernardo, J., Berger, J., Dawid, A., and Smith, A., editors, Bayesian Statistics 5, pages 407–425, Oxford, UK. Clarendon Press. Spirtes, P., Glymour, C., and Scheimes, R. (1993). Springer-Verlag, New York. Causation, Prediction and Search.

Spirtes, P. and Meek, C. (1995). Learning Bayesian networks with discrete variables from data. In Fayyad, U. and Uthurusamy, R., editors, Proc. of the Int’l. Conf. on Knowledge Discovery and Data Mining, pages 294–299, Montreal, Quebec. AAAI Press. Studeny, M. (1997). On marginalization, collapsability and precollapsability. In Benes, V. and Stepan, J., editors, Distributions with Given Marginals and Moment Problems, pages 191–198, Dordrecht. Kluwer. Tarjan, R. and Yannakakis, M. (1984). Simple linear time algorithms to test chordality of graphs, test acyclicity of hypergraphs and selectively reduce acyclic hypergraphs. SIAM Journal of Computing, 13:566–579. Verma, T. and Pearl, J. (1988). In?uence diagrams and d-separation. Technical report, Cognitive Systems Laboratory, UCLA. Verma, T. and Pearl, J. (1990). Equivalence and synthesis of causal models. In Bonissone, P., Henrion, M., Kanal, L., and Lemmer, J., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 255–268. Morgan Kaufmann.

BIBLIOGRAPHY

139

Wermuth, N. (1980). Linear recursive equations, covariance selection, and path analysis. Journal of the American Statistical Association, 75:963–972. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Wiley, New York. Wolk, E. (1962). The comparability graph of a tree. Proc. Am. Math. Soc., 13:789–795. Wolk, E. (1965). A note on “the comparability graph of a tree”. Proc. Am. Math. Soc., 16:17–20. Wormald, N. (1985). Counting labeled chordal graphs. Graphs and Combinatorics, 1:193– 200. Xiang, Y., Wong, S., and N.Cercone (1996). Critical remarks on single link search in learning belief networks. In Horvitz, E. and Jensen, F., editors, Proc. of the Conf. on Uncertainty in Arti?cial Intelligence, pages 564–571. Morgan Kaufmann.

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