Nevin’s Old Reading List (pre-2006)

  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom. Contr., 19:716-723.
  2. Akaike, H. (1983). Information measures and model seletion. Bulletin of the International Statistical Institute, 50, 277-290.
  3. Alvord, W. G., Drummond, J. E., Arthur. L.O., Biggar, R. J., Goedert, J. J., Levine, P. H.,  Murphy, E. L. Jr, Weiss, S. H., Blattner, W. A. (1988).    A method for predicting individual HIV infection status in the absence of clinical information.  AIDS Res Hum Retroviruses, 4(4):295-304.
  4. Ben-Dor, A., Friedman, N., and Yakhini, Z. (2001). Class discovery in gene expression data.  In Proc. 5th Annual Inter. Conf. on Computational Molecular Biology (RECOMB).
  5. Banfield, J. D. and Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49, 803-821.
  6. Bartholomew, D. J. and Knott, M. (1999). Latent variable models and factor analysis, 2nd edition. Kendall's Library of Statistics 7. London: Arnold.
  7. Blake, C.L. and Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine,  CA: University of California, Department of Information and Computer Science.
  8. Bohrnstedt, G. W. and Knoke D. (1994). Statistics for social data analysis (3rd Edition). F. E. Peacock Publishers Inc., Itasca, Illinois.
  9. Breese, J.,  Heckerman, D., and  Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering.  Proc. of the 14th Conference on Uncertainty in Artificial Intelligence..
  10. Cheeseman, P. and Stutz, J. (1995). Bayesian classification (AutoClass): Theory and results. In Fayyad, U., Piatesky-Shaoiro, G., Smyth, P., and Uthurusamy, R. (eds.), Advancesin Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA.
  11. Cheng, J., Greiner, R., Kelly, J., Bell, DA and Liu, W.(2002).  Learning Bayesian Networks from Data: an  Information-Theory Based Approach. The Artificial Intelligence Journal, 137: 43-90.
  12. Chickering, D. M. (1995). A transformational characterization of equivalent Bayesian network structures. In Proc. 11th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco, 87-98.
  13. Chickering, D. M. and Heckerman, D. (1997).  Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables.Machine Learning 29(2-3): 181-212.
  14. Chickering, D. M. (2002). Learning Equivalence Classes of Bayesian-Network Structures. Journal of Machine Learning Research, 2:445-498.
  15. Chickering,  D. M. (2002b). Optimal Structure Identification with Greedy Search. Technical Report MSR-TR-2002-10, Microsoft Research.
  16. Chow, C. K. and Liu, C. N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, IT-14(3): 462-467.
  17. Coleman, J. S. (1964). Introduction to Mathematical Sociology. London: Free Press.
  18. Connolly, D. (1993). Constructing hidden variables in Bayesian networks via conceptual learning. In 10th Int. Conf. on Machine Learning (ICML-93),  Amherst, MA, USA, 65-72.
  19. Cowell, R. G., Dawid, A. P.,  Lauritzen, S. L., and Spiegelhalter, D. J.  (1999). Probabilistic networks and expert systems, Springer.
  20. Dougherty, J.,   Kohavi, R.,  and  Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In Proc. of the  12th  International Conf. on Machine learning,  194-202.
  21. Durbin, R.,  Eddy, S.,  Krogh, A.,  and Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press.
  22. Eaton, W. W., Dryman, A., Sorenson, A., and McCutheon, A. (1989). DSM-III Major depressive disorder in the community: A latent class analysis of data from the NIMH epidemiologic catchment area programme. British Journal of Psychiatry, 155, 48-54.
  23. Elidan, G., Lotner, N., Friedman, N. and Koller, D. (2000). Discovering hidden variables: A structure-based approach. Advances in Neural Information Processing Systems 13 (NIPS-00), Denver, CO, USA, 479-485.
  24. Elidan, G. and N. Friedman (2001). Learning the dimensionality of hidden variables. In Proc. 17th Conf. on Uncertainty in Artificial Intelligence (UAI-01), Seattle, Washington, USA, 144-151.
  25. Espeland, M. A. and Handelman, S. L. (1989).  Using latent class models to characterize and assess relative error in discrete measurements. Biometrics, 45,   587-599.
  26. Everitt, B. S. (1993). Cluster Analysis. London: Edward Arnold.   [To Get]
  27. Ezawa, K. J. and  Schuermann, T. (1995).  Fraud/uncollectible debt detection using a Bayesian network based learning  system: A rare binary outcome with mixed data structures. In Proc. 11th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco,  157-166.
  28. Fraley, C. (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM J. Sci. Comput., 20 (1), 270-281.
  29. Friedman, N. (1997).  Learning belief networks in the presence of missing values and hidden variables. In Proc. of 14th Int. Conf. on Machine Learning (ICML), Nashville, TN, USA,  125-133.
  30. Friedman, N. (1998).  The Bayesian structural EM algorithm. In Proc. 14th Conf. on Uncertainty in Artificial Intelligence (UAI-98).
  31. Friedman, N., Geiger, D., and Goldszmidt, M. (1997).  Bayesian networks classifiers. Machine Learning, 29:131-163.
  32.  Friedman, N.,  Ninio, M.,   Pe'er, I.,  and  Pupko, T. (2001). A structural EM algorithm for phylogenetic inference.  In Proc. 5th Annual Int. Conf. on Computational Molecular Biology (RECOMB).
  33. Friedman, N.,  Ninio, M.,   Pe'er, I.,  and  Pupko, T. (2002).A structural EM algorithm for phylogenetic inference. Journal of Computational Biology, 9:331-353.
  34. Garrett, E. S. and  Zeger,  S. L. (2000).  Latent class model diagnosis. Biometrics, 56, 1055-1067.
  35.  Geiger, D.,   Heckerman, D.,  and C. Meek, C. (1996).  Asymptotic Model Selection for Directed Networks with Hidden Variables. in Proc. 12th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco,  158-168.
  36. Geiger, D.,  Heckerman, D.,  King, H., and  Meek, C. (2001). Stratified exponential families: Graphical models and model selection. The Annals of Statistics, 29 (1),:505-529.
  37. Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24: 229-252.
  38. Glymour, C. and Cooper, G. F.  (eds.) (1999). Computation, causation, and discovery. AAAI Press, Cambridge, Mass. : MIT Press
  39. Goodman, L. A. (1974a). The analysis of systems of qualitative variables when some of the variables are unobservable. Part I-A Modified latent structure approach. American Journal of Sociology, 7(5), 1179-1259.
  40. Goodman, L. A. (1974b).  Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika,  61, 215-231.
  41. Green, P. (1998). Penalized likelihood. In Encyclopedia of Statistical Sciences, Update Volume 3, S. Kotz, C. Read, D. L. Banks (eds.), 578-586, John Wiley \& Sons.
  42. Hagenaars, J. A. (1988).  Latent structure models with direct effects between indicators: local dependence models. Sociological Methods and Research,  16, 379-405.
  43. Hagenaars, J. A. and McCutcheon A. L. (eds.) (2002). Advances in latent class analysis. Cambridge University Press.
  44.  Hanson, R.,  Stutz, J.,  and  Cheeseman, P. (1991).  Bayesian classification with correlation and inheritance.  Proc.  12th. International Joint Conference on A.I. (IJCAI-91), Sydney, New South Wales, Australia, 2, 692-698, Morgan Kaufmann.
  45. Haughton, Dominique M. A. (1988). On the choice of a model to fit data from an exponential family. Annals of Statistics, 16(1), 342-355.
  46. Heckerman, D (1995). A tutorial on learning with Bayesian networks. In  Michael J. Jordan (ed.) (1998). Learning in graphical models. Kluwer Academic Publishers.
  47. Heckerman, D.,  Geiger, D.  and  Chickering, D. M. (1994). Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Proc. 10th Conf. Uncertainty in Artificial Intelligence, 293-301.
  48. Hofman, T. (1999). The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data. In Proc.  20th. International Joint Conference on A.I. (IJCAI-99).
  49. Jensen, F. V. (2001). Bayesian networks and decision graphs. Springer.
  50. Jordan, K. J. (ed.) (1998). Learning in graphical models. Kluwer Academic Publishers.
  51. Kass, R. and Raftery A. (1995). Bayes factors. Journal of the American Statistical Association, 90:773-795.
  52. Kervrann, C. and Heitz F. (1998). A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes. Graphical Models And Image Processing, 60(3), 173-195.
  53. Kaufman, L. and Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley and Sons, Inc..            [To Get]
  54. Kim, J. and T. Warnow. 1999. Tutorial on phylogenetic tree estimation. The 7th International Conference Intelligent Systems for Molecular Biology, Heidelberg.
  55. Kishino, H.,  Miyata, T.,  and Hasegawa, M.  (1990). Maximum likelihood inference of protein phylogeny and the origin of the chloroplasts. J. Mol. Evol. 31, 151-160.
  56.  KOCKA, T. and CASTELO, R. (2001). Improved Learning of Bayesian Networks. In  Proc. 17th Conf. on Uncertainty in Artificial Intelligence, 269-276.
  57. Kohlmann, T., and Formann, A. K. (1997). Using latent class models to analyze response patterns in epidemiologic mail surveys.  Rost, J. and  Langeheine, R. (eds.). Applications of latent trait and latent class models in the social sciences. Muenster: Waxman Verlag.
  58. Kohavi, R. and  John, G. (1997). Wrappers for feature subset selection.  Artificial Intelligence Journal, special issue on relevance, 97(1-2),  273-324.
  59. Kononenko, I. (1991). Semi-naive Bayesian classifier. In Proc. 6th European Working Session on Learning, Berlin: Springer-Verlag, 206-219.\
  60. Langley, P. and Sage, S. (1994). Induction of selective Bayesian classifiers. In Proc. 10th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco,  339-406.
  61. Lanterman, A. D. (2001). Schwarz, Wallace, and Rissanen: Intertwining themes in theories of model order estimation. International Statistical Review, 69(2), 185-212.
  62. Lauritzen, S. L.  (1995). The EM-algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 1,  191-201.
  63. Lazarsfeld, P. F., and Henry, N.W. (1968).  Latent structure analysis. Boston: Houghton Mifflin.
  64. McLachlan, G.J. and Krishnan, T. (1997). The EM algorithm and extensions. Wiley Interscience.
  65. Martin, J. and VanLehn, K. (1994). Discrete factor analysis: learning hidden variables in Bayesian networks. Technical Report LRGC_ONR-94-1, Department of Computer Science, University of Pittsburgh.
  66. Meila-Predoviciu, M. (1999). Learning with mixtures of trees, Ph.D. Dissertation, Department of Electrical Engineering and Computer Science, MIT.
  67. Meek, C. (1995). Strong completeness and faithfulness in Bayesian networks. In Proc. of 11th Conference on Uncertainty in Artificial Intelligence, 411-418.
  68. Meek, C. (1997). Graphical models: Selection causal and statistical models. Ph.D. Thesis, Carnegie Mellon University.
  69. Olave, M.,  Rajkovic, V.,  and  Bohanec, M. (1989).  An application for admission in public school systems. In I. Th. M. Snellen and W. B. H. J. van de  Donk and J.-P. Baquiast (eds.).  Expert Systems in Public  Administration,  Elsevier Science Publishers: North   Holland, 145-160.
  70. Pazzani, M. J. (1995). Searching for dependencies in Bayesian classifiers. In Proc. 5th Int. Workshop on AI and Statistics, Ft. Lauderdale, FL..
  71. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Palo Alto.
  72. Rusakov, D. and Geiger, D. (2002).   Asymptotic Model Selection for Naive Bayesian Networks. UAI-02.
  73. Rusakov, D. and Geiger, D. (2003). Automated Analytic Asymptotic Evaluation of Marginal Likelihood for Latent Models. UAI-03.
  74. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.
  75. Silva, R., S., Scheines, R.,   Glymour, C.,  and Spirtes P. (2003). Learning Measurement Models for Unobserved Variables. . In Proc. of 19th Conference on Uncertainty in Artificial Intelligence, 543-550.
  76. Silva, R., S., Scheines, R.,   Glymour, C.,  and Spirtes P. (2005). Learning the Structures of Linear Latent Variable Models.
  77. Singh, B. B., Berman, B. M., Simpson, R. L., and Annechild, A. (1998). Incidence of premenstrual syndrome and remedy usage: A national probability sample study. Alternative Therapies, 4(3): 75-79.
  78. Spirtes, P., Glymour, C., and Scheines, R. (1993).  Causation, prediction, and search. Springer-Verlag.
  79. Spirtes, P. and  Meek (1995). Learning Bayesian networks with discrete variables from data. In Proc. of First International Conference on Knowledge Discovery and Data Mining, 294-299.
  80. Sullivan, P.F., Smith, W. and Buchwald, D. (2002). Latent class analysis of symptoms associated with chronic fatigue syndrome and fibromyalgia. Psychological Medicine, 32, 881-888.
  81. Swofford, D. L. (1998). PAUP* 4.0 - Phylogenetic Analysis Using Parsimony (*and Other Methods). Sinauer Assoc., Sunderland, MA.
  82. Thrun, S.B. and 20 others  (1991). The MONK's Problems: A Performance Comparison of Different Learning Algorithms. CMU-CS-91-197, Carnegie Mellon University.
  83. Toutanova, K.,  Chen, F.,  Popat, K., and   Hofmann T. (2001). Text Classification in a Hierarchical Mixture Model for Small Training Sets. In Proc. 10th International Conference on Information and Knowledge Management.
  84. van der Putten, P. and van Someren, M. (eds.) (2002). COIL Challenge 2000: The insurance company case. Sentient Machine Research, Amsterdam.
  85. Verma, T. and Pearl, J. (1991). Equivalence and synthesis of causal models. In Proc. of 6th Conference on Uncertainty in Artificial Intelligence, 220-227.
  86. Vermunt, J.K. and Magidson,  J. (2000).  Latent GOLD User's Guide. Belmont, Mass.: Statistical Innovations, Inc..
  87. Vermunt, J.K. and Magidson,  J. (2002). Latent class cluster analysis. In Hagenaars, J. A. and McCutcheon A. L. (eds.).  Advances in latent class analysis. Cambridge University Press.  [To Get]
  88. Wasmus, A., Kindel, P., Mattussek, S. and Raspe, H. H. (1989). Activity and severity of rheumatoid arthritis in Hannover/FRG and in one regional referral center. Scandinavian Journal of Rheumatology, Suppl. 79, 33-44.
  89. Zupan, B.,   Bohanec, M.,   Bratko, I. and  Demsar, J. (1997). Machine learning by function decomposition. ICML-97.
  90. Zhang, N. L. (2002). Inference in Bayesian networks: The role of context-specific independence. International Journal of Information Technology & Decision Making, 1(1): 91-119.

 

Latent Variable Models

  1. Borsboom, D., Mellenbergh, G. J., and van He
  2. erden, J. (2003).The theoretical status of latent variables. {\em Psychological Review}, 110 (2), 203-219.
  3. Sobel, M. E. (1994). Causal Inference in latent variable models. In von Eye, A.  and  Clogg, C. C  (eds.) (1994).  Latent variables analysis: applications for developmental research. Thousand Oaks, Calif.: Sage, 3-35.
  4. Uebersax, J. (2000). A practical guide to local dependence in latent class models. http://ourworld.compuserve.com/homepages/jsuebersax/condep.htm.
  5. Uebersax, J. (2001). Latent class analysis frequently asked questions. http://ourworld.compuserve.com/homepages/jsuebersax/faq.htm.
  6. von Eye, A.  and  Clogg, C. C  (eds.) (1994).  Latent variables analysis: applications for developmental research. Thousand Oaks, Calif.: Sage.


 

·  Papers by Jay Magidson and Jeroen Vermunt on latent class modelling

·  Softare for Inference of evolutionary trees. Related to HLCM:  PROTML(Parent introduction)  ,  PAUP(Branch swapping)

·  D. L. Swofford, G. J. Olsen, P. J. Waddell, and D. M. Hillis. Molecular Systematics, chapter Phylogenetic Inference, pages 407--514.  Sinauer Associates, Inc., Sunderland, MA, 1996.

 

 

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