Average case complete problems
SIAM Journal on Computing
On the theory of average case complexity
Journal of Computer and System Sciences
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Statistical methods for speech recognition
Statistical methods for speech recognition
A design principles of a weighted finite-state transducer library
Theoretical Computer Science - Special issue on implementing automata
Optimal linguistic decoding is a difficult computational problem
Pattern Recognition Letters
Defense of the ansatz for dynamical hierarchies
Artificial Life
The consensus string problem and the complexity of comparing hidden Markov models
Journal of Computer and System Sciences - Computational biology 2002
Computational Complexity of Problems on Probabilistic Grammars and Transducers
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Parsing inside-out
Finite-state transducers in language and speech processing
Computational Linguistics
Computational complexity of probabilistic disambiguation by means of tree-grammars
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
On the computation of some standard distances between probabilistic automata
CIAA'06 Proceedings of the 11th international conference on Implementation and Application of Automata
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The problem of finding the most probable string for a distribution generated by a weighted finite automaton or a probabilistic grammar is related to a number of important questions: computing the distance between two distributions or finding the best translation (the most probable one) given a probabilistic finite state transducer. The problem is undecidable with general weights and is NP-hard if the automaton is probabilistic. We give a pseudo-polynomial algorithm which computes the most probable string in time polynomial in the inverse of the probability of the most probable string itself, both for probabilistic finite automata and probabilistic context-free grammars. We also give a randomised algorithm solving the same problem.