The solution of some random NP-hard problems in polynomial expected time
Journal of Algorithms
Graph rewriting: an algebraic and logic approach
Handbook of theoretical computer science (vol. B)
Monadic second-order evaluations on tree-decomposable graphs
Theoretical Computer Science - Special issue on selected papers of the International Workshop on Computing by Graph Transformation, Bordeaux, France, March 21–23, 1991
Approximating grammar probabilities: solution of a conjecture
Journal of the ACM (JACM)
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Graph Grammars and Their Application to Computer Science: 4th International Workshop, Bremen, Germany, March 5-9, 1990 Proceedings
Proceedings of the 3rd International Workshop on Graph-Grammars and Their Application to Computer Science
STOCHASTIC CONTEXT-FREE GRAMMARS FOR MODELLING RNA
STOCHASTIC CONTEXT-FREE GRAMMARS FOR MODELLING RNA
Applying Probability Measures to Abstract Languages
IEEE Transactions on Computers
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In a probabilistic graph grammar, each production has a probability attached to it. This induces a probability assigned to each derivation tree, and to each derived graph. Conditions for this probability function to be a probabilistic measure are discussed. The statistical properties of the generated language are investigated. We show how to compute the average size of an inductive function, and the probability of an inductive graph predicate. A relationship can be established between production probabilities and some statistical information of the generated language. This way, probabilities can be assigned to productions so that the generated graphs satisfy some statistical condition.