Results 11 to 20 of about 1,572 (217)
In this paper we consider the research and development of classifiers that are trained to predict the task solved by source code. Possible applications of such task detection algorithms include method name prediction, hardware–software partitioning ...
Artyom V. Gorchakov +2 more
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Putting Markov Chains Back into Markov Chain Monte Carlo [PDF]
Markov chain theory plays an important role in statistical inference both in the formulation of models for data and in the construction of efficient algorithms for inference. The use of Markov chains in modeling data has a long history, however the use of Markov chain theory in developing algorithms for statistical inference has only become popular ...
Richard J. Barker, Matthew R. Schofield
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Parametric Markov Chains: PCTL Complexity and Fraction-free Gaussian Elimination [PDF]
Parametric Markov chains have been introduced as a model for families of stochastic systems that rely on the same graph structure, but differ in the concrete transition probabilities. The latter are specified by polynomial constraints for the parameters.
Lisa Hutschenreiter +2 more
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A Definition Scheme for Quantitative Bisimulation [PDF]
FuTS, state-to-function transition systems are generalizations of labeled transition systems and of familiar notions of quantitative semantical models as continuous-time Markov chains, interactive Markov chains, and Markov automata.
Diego Latella +2 more
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Optimal control of multiple Markov-switching stochastic systems with numerical applications
In this article the authors set up an optimal control framework for a hybrid stochastic system with dual or multiple Markov switching diffusion processes, while Markov chains governing these switching diffusions are not identical as assumed in the ...
Jianmin Shi
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Information geometry of Markov Kernels: a survey
Information geometry and Markov chains are two powerful tools used in modern fields such as finance, physics, computer science, and epidemiology. In this survey, we explore their intersection, focusing on the theoretical framework.
Geoffrey Wolfer, Shun Watanabe
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We study irreducible time-homogenous Markov chains with finite state space in discrete time. We obtain results on the sensitivity of the stationary distribution and other statistical quantities with respect to perturbations of the transition matrix. We define a new closeness relation between transition matrices, and use graph-theoretic techniques, in ...
Eilon Solan, Nicolas Vieille
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Optimal choice of word length when comparing two Markov sequences using a χ 2-statistic
Background Alignment-free sequence comparison using counts of word patterns (grams, k-tuples) has become an active research topic due to the large amount of sequence data from the new sequencing technologies.
Xin Bai +4 more
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The article introduces constraint Markov chains as a new tool for specification. They are a generalization of interval Markov chains. Interval Markov chains extend Markov chains by labeling transitions with intervals, implying that each transition probability needs to be within the according interval.
Caillaud, Benoit +5 more
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We introduce the space of virtual Markov chains (VMCs) as a projective limit of the spaces of all finite state space Markov chains (MCs), in the same way that the space of virtual permutations is the projective limit of the spaces of all permutations of finite sets.We introduce the notions of virtual initial distribution (VID) and a virtual transition ...
Evans, Steven, Jaffe, Adam Q.
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