Probabilistic inference, inference of probabilistic machines

[Abe and Warmuth, 1992]
N. Abe and M. Warmuth. On the computational complexity of approximating distributions by probabilistic automata. Machine Learning, 9:205-260, 1992.

[Angluin, 1988]
D. Angluin. Identifying languages from stochastic examples. Technical Report YALEU/DCS/RR-614, Yale University, March 1988.

[Carrasco and Oncina, 1994]
R. Carrasco and J. Oncina. Learning stochastic regular grammars by means of a state merging method. In Grammatical Inference and Applications, ICGI'94, number 862 in Lecture Notes in Artificial Intelligence, pages 139-150. Springer Verlag, 1994.

[Cook and Rozenfeld, 1974]
C.M. Cook and A. Rozenfeld. Some experiments in grammatical inference. In Computer Oriented Learning Process, pages 157-171, Bonas, France, 1974. NATO Advanced Study Institute.

[Dean et al., 1995]
T. Dean, D. Angluin, k. Basye, S. Engelson, l. Kaelbling, E. Kokkevis, and O. Maron. Inferring finite automata with stochastic output functions and an application to Map learning. Machine Learning, 18:81-108, 1995.

[Freund et al., 1993]
Y. Freund, M. Kearns, D. Ron, R. Rubinfeld, R. Schapire, and L. Sellie. Efficient learning of typical automata from random walks. In 25th ACM Symposium on the Theory of Computing, pages 315-324, 1993.

[Gaines, 1979]
F.J. Gaines. Maryanski's grammatical inferencer. IEEE Transactions on Computers, 27(1):62-64, 1979.

[Grünwald, 1994]
P. Grünwald. Grammar inference and the minimum description length principle. NeuroCOLT Technical Report Series NC-TR-94-015, ESPRIT Working Group in Neural and Computational Learning, 1994.

[Horning, 1969]
J.J. Horning. A Study of Grammatical Inference. Ph. D. dissertation, Computer Science Department, Stanford University, Stanford, California, 1969.

[Kearns et al., 1994]
M.J. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R.E. Schapire, and L. Sellie. On the learnability of discrete distributions. In Proc. of the 25th Annual ACM Symposium on Theory of Computing, pages 273-282, 1994.

[Maryanski, 1974]
F.J. Maryanski. Inference of Probabilistic Grammars. Ph. D. dissertation, University of Connecticut, 1974.

[Ron et al., 1995]
D. Ron, Y. Singer, and N. Tishby. On the learnability and usage of acyclic probabilistic automata. In Computational Learning Theory, COLT'95, pages 31-40, 1995.

[Stolcke and Omohundro, 1993]
A. Stolcke and S.M. Omohundro. Hidden Markov Model induction by bayesian model merging. In C.L. Giles, S.J. Hanton, and J.D. Cowan, editors, Advances in Neural Information Processing Systems. MOrgan Kauffman, 1993.

[Stolcke and Omohundro, 1994a]
A. Stolcke and S. Omohundro. Inducing probabilistic grammars by bayesian model merging. In Grammatical Inference and Applications, ICGI'94, number 862 in Lecture Notes in Artificial Intelligence, pages 106-118. Springer Verlag, 1994.

[Stolcke and Omohundro, 1994b]
A. Stolcke and S.M. Omohundro. Best-first model merging for Hidden Markov Model induction. Technical Report TR-94-003, ICSI, January 1994.

[Stolcke, 1994]
A. Stolcke. Bayesian Learning of Probabilistic Language Models. Ph. D. dissertation, University of California, 1994.

[Tzeng, 1989a]
W.-G. Tzeng. The equivalence and learning of probabilistic automata. In 30th Annual Symposium on Fundations of Computer Science, pages 268-273, 1989.

[Tzeng, 1989b]
W.-G. Tzeng. A polynomial-time algorithm for the equivalence of probabilistic automata. SIAM Journal Comput., 21(2):216-227, 1989.

pdupont@info.ucl.ac.be