Bibliography

1

Abramson, B., Brown, J., Murphy, A., & Winkler, R. L. (1996).
Hailfinder: A Bayesian system for forecasting severe weather.
International Journal of Forecasting, 12, 57-71.

2

Acid, S., & de Campos, L. M. (2000).
Learning right sized belief networks by means of a hybrid methodology.
Lecture Notes in Artificial Intelligence, 1910, 309-315.

3

Acid, S., & de Campos, L. M. (2001).
A hybrid methodology for learning belief networks: Benedict.
International Journal of Approximate Reasoning, 27, 235-262.

4

Andersson, S., Madigan, D., & Perlman, M. (1997).
A Characterization of Markov equivalence classes for acyclic digraphs.
Annals of Statistics, 25, 505-541.

5

Beinlich, I. A., Suermondt, H. J., Chavez, R. M., & Cooper, G. F. (1989).
The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks.
In Proceedings of the European Conference on Artificial Intelligence in Medicine, 247-256.

6

Binder, J., Koller, D., Russell, S., & Kanazawa, K. (1997).
Adaptive probabilistic networks with hidden variables.
Machine Learning, 29, 213-244.

7

Blanco, R., Inza, I., & Larrañaga, P. (2003).
Learning Bayesian networks in the space of structures by estimation of distribution algorithms.
International Journal of Intelligent Systems, 18, 205-220.

8

Bouckaert, R. R. (1993).
Belief networks construction using the minimum description length principle.
Lecture Notes in Computer Science, 747, 41-48.

9

Bouckaert, R. R. (1995).
Bayesian belief networks: from construction to inference.
Ph.D. thesis, University of Utrecht.

10

Buntine, W. (1991).
Theory refinement of Bayesian networks.
In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, 52-60.

11

Buntine, W. (1994).
Operations for learning with graphical models.
Journal of Artificial Intelligence Research, 2, 159-225.

12

Buntine, W. (1996).
A guide to the literature on learning probabilistic networks from data.
IEEE Transactions on Knowledge and Data Engineering, 8, 195-210.

13

Cheng, J., Bell, D. A., & Liu, W. (1997).
An algorithm for Bayesian belief network construction from data.
In Proceedings of AI and STAT'97, 83-90.

14

Cheng, J., Bell, D. A., & Liu, W. (1998).
Learning Bayesian networks from data: An efficient approach based on information theory.
Tech. rep., University of Alberta.

15

Chickering, D. M. (1995).
A transformational characterization of equivalent Bayesian network structures.
In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 87-98.

16

Chickering, D. M. (1996).
Learning equivalence classes of Bayesian network structures.
In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, 150-157.

17

Chickering, D. M. (2002).
Learning equivalence classes of Bayesian network structures.
Journal of Machine Learning Research, 2, 445-498.

18

Chickering, D. M., Geiger, D., & Heckerman, D. (1995).
Learning Bayesian networks: Search methods and experimental results.
In Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics, 112-128.

19

Chow, C., & Liu, C. (1968).
Approximating discrete probability distributions with dependence trees.
IEEE transactions on Information Theory, 14, 462-467.

20

Cooper, G. F., & Herskovits, E. (1992).
A Bayesian method for the induction of probabilistic networks from data.
Machine Learning, 9, 309-348.

21

Dash, D., & Druzdzel, M. (1999).
A hybrid anytime algorithm for the construction of causal models from sparse data.
In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 142-149.

22

de Campos, L. M. (1998).
Independency relationships and learning algorithms for singly connected networks.
Journal of Experimental and Theoretical Artificial Intelligence, 10, 511-549.

23

de Campos, L. M., Fernández-Luna, J. M., Gámez, J. A., & Puerta, J. M. (2002).
Ant colony optimization for learning Bayesian networks.
International Journal of Approximate Reasoning, 31, 291-311.

24

de Campos, L. M., Fernández-Luna, J. M., & Puerta, J. M. (2002).
Local search methods for learning Bayesian networks using a modified neighborhood in the space of dags.
Lecture Notes in Computer Science, 2527, 182-192.

25

de Campos, L. M., Fernández-Luna, J. M., & Puerta, J. M. (2003).
An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests.
International Journal of Intelligent Systems, 18, 221-235.

26

de Campos, L. M., Gámez, J. A., & Puerta, J. M. (in press).
Learning Bayesian networks by ant colony optimisation: Searching in two different spaces.
Mathware and Soft Computing.

27

de Campos, L. M., & Huete, J. F. (1997).
On the use of independence relationships for learning simplified belief networks.
International Journal of Intelligent Systems, 12, 495-522.

28

de Campos, L. M., & Huete, J. F. (2000).
A new approach for learning belief networks using independence criteria.
International Journal of Approximate Reasoning, 24, 11-37.

29

de Campos, L. M., & Huete, J. F. (2000).
Approximating causal orderings for Bayesian networks using genetic algorithms and simulated annealing.
In Proceedings of the Eighth IPMU Conference, 333-340.

30

de Campos, L. M., & Puerta, J. M. (2001).
Stochastic local and distributed search algorithms for learning belief networks.
In Proceedings of the III International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Model, 109-115.

31

de Campos, L. M., & Puerta, J. M. (2001).
Stochastic local search algorithms for learning belief networks: Searching in the space of orderings.
Lecture Notes in Artificial Intelligence, 2143, 228-239.

32

Dor, D., & Tarsi, M. (1992).
A simple algorithm to construct a consistent extension of a partially oriented graph.
Tech. rep. R-185, Cognitive Systems Laboratory, Department of Computer Science, UCLA.

33

Friedman, N., & Goldszmidt, M. (1996).
Learning Bayesian networks with local structure.
In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, 252-262.

34

Friedman, N., & Koller, D. (2000).
Being Bayesian about network structure.
In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 201-210.

35

Friedman, N., Nachman, I., & Peér, D. (1999).
Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm.
In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 206-215.

36

Geiger, D., & Heckerman, D. (1995).
A characterisation of the Dirichlet distribution with application to learning Bayesian networks.
In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 196-207.

37

Geiger, D., Paz, A., & Pearl, J. (1990).
Learning causal trees from dependence information.
In Proceedings of AAAI-90, 770-776.

38

Geiger, D., Paz, A., & Pearl, J. (1993).
Learning simple causal structures.
International Journal of Intelligent Systems, 8, 231-247.

39

Gillispie, S. B., & Perlman, M. D. (2001).
Enumerating Markov equivalence classes of acyclic digraphs models.
In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 171-177.

40

Glover, F. (1989).
Tabu search, Part I.
ORSA Journal of Computing, 1, 190-206.

41

Heckerman, D. (1996).
Bayesian networks for knowledge discovery.
In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining. Cambridge: MIT Press, 273-305.

42

Heckerman, D., Geiger, D., & Chickering, D. M. (1995).
Learning Bayesian networks: The combination of knowledge and statistical data.
Machine Learning, 20, 197-243.

43

Herskovits, E. (1991).
Computer-based probabilistic networks construction.
Ph.D thesis, Medical Information Sciences, Stanford University.

44

Herskovits, E., & Cooper, G. F. (1990).
Kutató: An entropy-driven system for the construction of probabilistic expert systems from databases.
In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, 54-62.

45

Huete, J. F., & de Campos, L. M. (1993).
Learning causal polytrees.
Lecture Notes in Computer Science, 747, 180-185.

46

Jensen, F. V. (1996).
An Introduction to Bayesian Networks.
UCL Press.

47

Kocka, T., & Castelo, R. (2001).
Improved learning of Bayesian networks.
In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 269-276.

48

Kohavi, R., John, G., Long, R., Manley, D., & Pfleger, K. (1994).
MLC++: A machine learning library in C++.
In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, 740-743.

49

Kullback, S. (1968).
Information Theory and Statistics.
Dover Publication.

50

Lam, W., & Bacchus, F. (1994).
Learning Bayesian belief networks. An approach based on the MDL principle.
Computational Intelligence, 10, 269-293.

51

Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R., & Kuijpers, C. (1996).
Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 912-926.

52

Larrañaga, P., Kuijpers, C., & Murga, R. (1996).
Learning Bayesian network structures by searching for the best ordering with genetic algorithms.
IEEE Transactions on System, Man and Cybernetics, 26, 487-493.

53

Madigan, D., Anderson, S. A., Perlman, M. D., & Volinsky, C. T. (1996).
Bayesian model averaging and model selection for Markov equivalence classes of acyclic digraphs.
Communications in Statistics - Theory and Methods, 25, 2493-2520.

54

Madigan, D., & Raftery, A. (1994).
Model selection and accounting for model uncertainty in graphical models using Occam's window.
Journal of the American Statistics Association, 89, 1535-1546.

55

Meek, C. (1995).
Causal inference and causal explanation with background knowledge.
In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 403-410.

56

Muntenau, P., & Cau, D. (2000).
Efficient score-based learning of equivalence classes of Bayesian networks.
Lecture Notes in Artificial Intelligence, 1910, 96-105.

57

Myers, J. W., Laskey, K. B., & Levitt, T. (1999).
Learning Bayesian networks from incomplete data with stochastic search algorithms.
In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 476-485.

58

Pearl, J. (1988).
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
San Mateo: Morgan Kaufmann.

59

Pearl, J., & Verma, T. S. (1990).
Equivalence and synthesis of causal models.
In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, 220-227.

60

Puerta, J. M. (2001).
Métodos locales y distribuidos para la construcción de redes de creencia estáticas y dinámicas (in Spanish).
Ph.D. thesis, Department of Computer Science and Artificial Intelligence, University of Granada.

61

Ramoni, M., & Sebastiani, P. (1997).
Learning Bayesian networks from incomplete databases.
In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 401-408.

62

Rebane, G., & Pearl, J. (1987).
The recovery of causal poly-trees from statistical data.
In L.N. Kanal, T.S. Levitt, J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 3, Amsterdam: North-Holland, 222-228.

63

Schwarz, G. (1978).
Estimating the dimension of a model.
Annals of Statistics, 6, 461-464.

64

Singh, M., & Valtorta, M. (1993).
An algorithm for the construction of Bayesian network structures from data.
In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 259-265.

65

Singh, M., & Valtorta, M. (1995).
Construction of Bayesian network structures from data: A brief survey and an efficient algorithm.
International Journal of Approximate Reasoning, 12, 111-131.

66

Spirtes, P., Glymour, C., & Scheines, R. (1993).
Causation, Prediction and Search.
Lecture Notes in Statistics 81, New York: Springer Verlag.

67

Spirtes, P., & Meek, C. (1995).
Learning Bayesian networks with discrete variables from data.
In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 294-299.

68

Steck, H. (2000).
On the use of skeletons when learning in Bayesian networks.
In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 558-565.

69

Suzuki, J. (1993).
A construction of Bayesian networks from databases based on the MDL principle.
In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 266-273.

70

Suzuki, J. (1996).
Learning Bayesian belief networks based on the minimum description length principle: An efficient algorithm using the B&B technique.
In Proceedings of the Thirteenth International Conference on Machine Learning, 462-470.

71

Tian, J. (2000).
A branch-and-bound algorithm for MDL learning Bayesian neworks.
In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 580-587.

72

Verma, T., & Pearl, J. (1990).
Causal networks: Semantics and expressiveness.
In R.D. Shachter, T.S. Lewitt, L.N. Kanal, J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence, 4, Amsterdam: North-Holland, 69-76.

73

Wermuth, N., & Lauritzen, S. (1983).
Graphical and recursive models for contingence tables.
Biometrika, 72, 537-552.

74

Wong, M. L., Lam, W., & Leung, K. S. (1999).
Using evolutionay computation and minimum description length principle for data mining of probabilistic knowledge.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 174-178.