Search results for "dynamical sources"

showing 3 items of 3 documents

Context Trees, Variable Length Markov Chains and Dynamical Sources

2012

Infinite random sequences of letters can be viewed as stochastic chains or as strings produced by a source, in the sense of information theory. The relationship between Variable Length Markov Chains (VLMC) and probabilistic dynamical sources is studied. We establish a probabilistic frame for context trees and VLMC and we prove that any VLMC is a dynamical source for which we explicitly build the mapping. On two examples, the "comb" and the "bamboo blossom", we find a necessary and sufficient condition for the existence and the uniqueness of a stationary probability measure for the VLMC. These two examples are detailed in order to provide the associated Dirichlet series as well as the genera…

Discrete mathematicsPure mathematicsStationary distributionMarkov chain010102 general mathematicsProbabilistic dynamical sourcesProbabilistic logicContext (language use)Information theoryVariable length Markov chains01 natural sciencesMeasure (mathematics)Occurrences of words[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010104 statistics & probabilitysymbols.namesakesymbolsUniquenessDynamical systems of the intervalDirichlet series0101 mathematics[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Dirichlet seriesMathematics
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Variable length Markov chains and dynamical sources

2010

Infinite random sequences of letters can be viewed as stochastic chains or as strings produced by a source, in the sense of information theory. The relationship between Variable Length Markov Chains (VLMC) and probabilistic dynamical sources is studied. We establish a probabilistic frame for context trees and VLMC and we prove that any VLMC is a dynamical source for which we explicitly build the mapping. On two examples, the ``comb'' and the ``bamboo blossom'', we find a necessary and sufficient condition for the existence and the unicity of a stationary probability measure for the VLMC. These two examples are detailed in order to provide the associated Dirichlet series as well as the gener…

MSC 60J05 MSC 37E05[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Probability (math.PR)[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][ MATH.MATH-DS ] Mathematics [math]/Dynamical Systems [math.DS]Probabilistic dynamical sources[MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS]Dynamical Systems (math.DS)Variable length Markov chainsOccurrences of words[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]60J05 37E05FOS: MathematicsMathematics - Dynamical SystemsDynamical systems of the intervalDirichlet series[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
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Statistical properties of general Markov dynamical sources: applications to information theory

2004

In \textitDynamical sources in information theory: fundamental intervals and word prefixes, B. Vallée studies statistical properties of words generated by dynamical sources. This is done using generalized Ruelle operators. The aim of this article is to generalize sources for which the results hold. First, we avoid the use of Grotendieck theory and Fredholm determinants, this allows dynamical sources that cannot be extended to a complex disk or that are not analytic. Second, we consider Markov sources: the language generated by the source over an alphabet \textbfM is not necessarily \textbfM^*.

dynamical sourcesGeneral Computer ScienceMarkov chainlcsh:Mathematicstransfer operator[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM][INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]lcsh:QA1-939Information theoryTheoretical Computer SciencePrefixAlgebra[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]Markov sourcesTransfer operatorDiscrete Mathematics and CombinatoricsAlphabetWord (computer architecture)Mathematicsinformation theory
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