A space-efficient simulation algorithm on probabilistic automata
نویسندگان
چکیده
منابع مشابه
Space-Efficient Deterministic Simulation of Probabilistic Automata
Given a description of a probabilistic automaton (one-head probabilistic nite automaton or probabilistic Turing machine) and an input string x of length n, we ask how much space does a deterministic Turing machine need in order to decide the acceptance of the input string by that automaton? The question is interesting even in the case of one-head one-way probabilistic nite automata (PFA). We ca...
متن کاملA Space-Efficient Probabilistic Simulation Algorithm
In the context of probabilistic automata, time efficient algorithms for probabilistic simulations have been proposed lately. The space complexity thereof is quadratic in the size of the transition relation, thus space requirements often become the practical bottleneck. In this paper, we exploit ideas from [6] to arrive at a space-efficient algorithm for computing probabilistic simulations based...
متن کاملCorrecting a Space-Efficient Simulation Algorithm
Although there are many efficient algorithms for calculating the simulation preorder on finite Kripke structures, only two have been proposed of which the space complexity is of the same order as the size of the output of the algorithm. Of these, the one with the best time complexity exploits the representation of the simulation problem as a generalised coarsest partition problem. It is based o...
متن کاملEfficient Pruning of Probabilistic Automata
Applications of probabilistic grammatical inference are limited due to time and space consuming constraints. In statistical language modeling, for example, large corpora are now available and lead to managing automata with millions of states. We propose in this article a method for pruning automata (when restricted to tree based structures) which is not only efficient (sub-quadratic) but that a...
متن کاملMenger probabilistic normed space is a category topological vector space
In this paper, we formalize the Menger probabilistic normed space as a category in which its objects are the Menger probabilistic normed spaces and its morphisms are fuzzy continuous operators. Then, we show that the category of probabilistic normed spaces is isomorphicly a subcategory of the category of topological vector spaces. So, we can easily apply the results of topological vector spaces...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information and Computation
سال: 2016
ISSN: 0890-5401
DOI: 10.1016/j.ic.2016.04.002