-
3
-
-
84951647142
-
-
Buntine, W. (1991). Theory refinement on Bayesian networks. In Proceedings of Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 52–60. Morgan Kaufmann.
-
-
-
-
5
-
-
84951647143
-
-
Chickering, D. (1995a). A transformational characterization of equivalent Bayesian-network structures. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 87–98. Morgan Kaufmann.
-
-
-
-
7
-
-
84951647144
-
-
Chickering, D., Geiger, D., & Heckerman, D. (1995). Learning Bayesian networks: Search methods and experimental results. In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, pages 112–128. Society for Artificial Intelligence in Statistics.
-
-
-
-
10
-
-
84951647145
-
-
Cooper, G. & Herskovits, E. (January, 1991). A Bayesian method for the induction of probabilistic networks from data. Technical Report SMI-91-1, Section on Medical Informatics, Stanford University.
-
-
-
-
14
-
-
84951647146
-
-
Druzdzel, M. & Simon, H. (1993). Causality in Bayesian belief networks. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 3–11. Morgan Kaufmann.
-
-
-
-
18
-
-
85025882433
-
-
Gabow, H., Galil, Z., & Spencer, T. (1984). Efficient implementation of graph algorithms using contraction. In Proceedings of FOCS.
-
-
-
-
19
-
-
84951647148
-
-
Geiger, D. & Heckerman, D. (1994). Learning Gaussian networks. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 235–243. Morgan Kaufmann.
-
-
-
-
20
-
-
84951647149
-
-
Geiger, D. & Heckerman, D. (1995). A characterization of the Dirichlet distribution with application to learning Bayesian networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 196–207. Morgan Kaufmann.
-
-
-
-
22
-
-
84951647150
-
-
Heckerman, D. (1995). A Bayesian approach for learning causal networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 285–295. Morgan Kaufmann.
-
-
-
-
23
-
-
84951647151
-
-
Heckerman, D. & Geiger, D. (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 274–284. Morgan Kaufmann.
-
-
-
-
24
-
-
84951647152
-
-
Heckerman, D., Geiger, D., & Chickering, D. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 293–301. Morgan Kaufmann.
-
-
-
-
26
-
-
84951647153
-
-
Heckerman, D. & Shachter, R. (1995). A definition and graphical representation of causality. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 262–273. Morgan Kaufmann.
-
-
-
-
27
-
-
84951647154
-
-
Höffgen, K. (revised 1993). Learning and robust learning of product distributions. Technical Report 464, Fachbereich Informatik, Universität Dortmund.
-
-
-
-
29
-
-
0023822855
-
Uncertainty about probability: A decision-analysis perspective
-
(1988)
Risk Analysis
, vol.8
, pp. 91-98
-
-
Howard, R.1
-
31
-
-
84951647156
-
-
Johnson (1985). How fast is local search? In FOCS, pages 39–42.
-
-
-
-
32
-
-
84981575514
-
A simple derivation of Edmond's algorithm for optimal branchings
-
(1971)
Networks
, vol.1
, pp. 265-272
-
-
Karp, R.1
-
36
-
-
84951647157
-
-
Lam, W. & Bacchus, F. (1993). Using causal information and local measures to learn Bayesian networks. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 243–250. Morgan Kaufmann.
-
-
-
-
38
-
-
85025857776
-
-
Madigan, D. & Raftery, A. (1994). Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association, 89.
-
-
-
-
39
-
-
84951647159
-
-
Matzkevich, I. & Abramson, B. (1993). Deriving a minimal I-map of a belief network relative to a target ordering of its nodes. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 159–165. Morgan Kaufmann.
-
-
-
-
42
-
-
0002838962
-
A theory of inferred causation
-
J., Allen, R., Fikes, E., Sandewall, Morgan Kaufmann, New York
-
(1991)
Knowledge Representation and Reasoning: Proceedings of the Second International Conference
, pp. 441-452
-
-
Pearl, J.1
Verma, T.2
-
46
-
-
84951647160
-
-
Spirtes, P. & Meek, C. (1995). Learning Bayesian networks with discrete variables from data. In Proceedings of First International Conference on Knowledge Discovery and Data Mining, Montreal, QU. Morgan Kaufmann.
-
-
-
-
47
-
-
84951647161
-
-
Suzuki, J. (1993). A construction of Bayesian networks from databases based on an MDL scheme. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 266–273. Morgan Kaufmann.
-
-
-
-
50
-
-
84951647162
-
-
Verma, T. & Pearl, J. (1990). Equivalence and synthesis of causal models. In Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 220–227. Morgan Kaufmann.
-
-
-
-
52
-
-
84951647163
-
-
York, J. (1992). Bayesian methods for the analysis of misclassified or incomplete multivariate discrete data. PhD thesis, Department of Statistics, University of Washington, Seattle.
-
-
-
|