Search results for "Variable"

showing 10 items of 1674 documents

On the size of transducers for bidirectional decoding of prefix codes

2012

In a previous paper [L. Giambruno and S. Mantaci, Theoret. Comput. Sci. 411 (2010) 1785–1792] a bideterministic transducer is defined for the bidirectional deciphering of words by the method introduced by Girod [ IEEE Commun. Lett. 3 (1999) 245–247]. Such a method is defined using prefix codes. Moreover a coding method, inspired by the Girod’s one, is introduced, and a transducer that allows both right-to-left and left-to-right decoding by this method is defined. It is proved also that this transducer is minimal. Here we consider the number of states of such a transducer, related to some features of the considered prefix code X . We find some bounds of such a number of states in relation wi…

Discrete mathematicsPrefix codeBlock codeSettore INF/01 - InformaticaGeneral MathematicsConcatenated error correction codeprefix codeList decodingSerial concatenated convolutional codesSequential decodingLinear codeComputer Science ApplicationsPrefixbilateral decodingVariable length codetransducersAlgorithmComputer Science::Formal Languages and Automata TheorySoftwareMathematics
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Weakly uniformly continuous holomorphic functions and the approximation property

2001

Abstract We study the approximation property for spaces of Frechet and Gâteaux holomorphic functions which are weakly uniformly continuous on bounded sets. We show when U is a balanced open subset of a Baire or barrelled metrizable locally convex space, E , that the space of holomorphic functions which are weakly uniformly continuous on U -bounded sets has the approximation property if and only if the strong dual of E , E ′ b , has the approximation property. We also characterise the approximation property for these spaces of vector-valued holomorphic functions in terms of the tensor product of the corresponding space of scalar-valued holomorphic functions and the range space.

Discrete mathematicsPure mathematicsMathematics(all)Approximation propertyMathematics::Complex VariablesGeneral MathematicsHolomorphic functionSpace (mathematics)Identity theoremUniform continuityTensor productBergman spaceBounded functionMathematicsIndagationes Mathematicae
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Holomorphically ultrabornological spaces and holomorphic inductive limits

1987

Abstract The holomorphically ultrabornological spaces are introduced. Their relation with other holomorphically significant classes of locally convex spaces is established and separating examples are given. Some apparently new properties of holomorphically barrelled spaces are included and holomorphically ultrabornological spaces are utilized in a problem posed by Nachbin.

Discrete mathematicsPure mathematicsMathematics::Complex VariablesApplied MathematicsLocally convex topological vector spaceHolomorphic functionMathematics::Symplectic GeometryAnalysisMathematicsJournal of Mathematical Analysis and Applications
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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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On a representation theorem for finitely exchangeable random vectors

2016

A random vector $X=(X_1,\ldots,X_n)$ with the $X_i$ taking values in an arbitrary measurable space $(S, \mathscr{S})$ is exchangeable if its law is the same as that of $(X_{\sigma(1)}, \ldots, X_{\sigma(n)})$ for any permutation $\sigma$. We give an alternative and shorter proof of the representation result (Jaynes \cite{Jay86} and Kerns and Sz\'ekely \cite{KS06}) stating that the law of $X$ is a mixture of product probability measures with respect to a signed mixing measure. The result is "finitistic" in nature meaning that it is a matter of linear algebra for finite $S$. The passing from finite $S$ to an arbitrary one may pose some measure-theoretic difficulties which are avoided by our p…

Discrete mathematicsRepresentation theoremMultivariate random variableApplied MathematicsSigned measureProbability (math.PR)010102 general mathematicsSpace (mathematics)01 natural sciencesMeasure (mathematics)60G09 (Primary) 60G55 62E99 (Secondary)010104 statistics & probabilityHomogeneous polynomialFOS: Mathematics0101 mathematicsMathematics - ProbabilityAnalysisMixing (physics)MathematicsProbability measureJournal of Mathematical Analysis and Applications
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QUANTITATIVE CONVERGENCE RATES FOR SUBGEOMETRIC MARKOV CHAINS

2015

We provide explicit expressions for the constants involved in the characterisation of ergodicity of subgeometric Markov chains. The constants are determined in terms of those appearing in the assumed drift and one-step minorisation conditions. The results are fundamental for the study of some algorithms where uniform bounds for these constants are needed for a family of Markov kernels. Our results accommodate also some classes of inhomogeneous chains.

Discrete mathematicsStatistics and ProbabilityMarkov chain mixing timeMarkov chainVariable-order Markov modelGeneral Mathematicsta111Markov chain010102 general mathematicsErgodicity01 natural sciencesInhomogeneous010104 statistics & probability60J05Polynomial ergodicitySubgeometric ergodicityConvergence (routing)60J22Examples of Markov chainsStatistical physics0101 mathematicsStatistics Probability and UncertaintyMathematics
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Relations between structure and estimators in networks of dynamical systems

2011

The article main focus is on the identification of a graphical model from time series data associated with different interconnected entities. The time series are modeled as realizations of stochastic processes (representing nodes of a graph) linked together via transfer functions (representing the edges of the graph). Both the cases of non-causal and causal links are considered. By using only the measurements of the node outputs and without assuming any prior knowledge of the network topology, a method is provided to estimate the graph connectivity. In particular, it is proven that the method determines links to be present only between a node and its “kins”, where kins of a node consist of …

Discrete mathematicsTheoretical computer scienceDirected graphStrength of a graphSettore ING-INF/04 - AutomaticaLeast squares approximation Network topology Random variables Stochastic processes TopologyGraph (abstract data type)Graph propertyNull graphRandom geometric graphComplement graphConnectivityMathematicsIEEE Conference on Decision and Control and European Control Conference
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Efficient CNF Encoding of Boolean Cardinality Constraints

2003

In this paper, we address the encoding into CNF clauses of Boolean cardinality constraints that arise in many practical applications. The proposed encoding is efficient with respect to unit propagation, which is implemented in almost all complete CNF satisfiability solvers. We prove the practical efficiency of this encoding on some problems arising in discrete tomography that involve many cardinality constraints. This encoding is also used together with a trivial variable elimination in order to re-encode parity learning benchmarks so that a simple Davis and Putnam procedure can solve them.

Discrete mathematicsTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESCardinalityUnit propagationComputer scienceConstrained optimizationData_CODINGANDINFORMATIONTHEORYVariable eliminationComputer Science::Computational ComplexityConjunctive normal formBoolean data typeSatisfiability
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Probabilistic Interpretations of Predicates

2016

In classical logic, any m-ary predicate is interpreted as an m-argument two-valued relation defined on a non-empty universe. In probability theory, m-ary predicates are interpreted as probability measures on the mth power of a probability space. m-ary probabilistic predicates are equivalently semantically characterized as m-dimensional cumulative distribution functions defined on \(\mathbb {R}^m\). The paper is mainly concerned with probabilistic interpretations of unary predicates in the algebra of cumulative distribution functions defined on \(\mathbb {R}\). This algebra, enriched with two constants, forms a bounded De Morgan algebra. Two logical systems based on the algebra of cumulative…

Discrete mathematicsUnary operationComputer Science::Logic in Computer ScienceCumulative distribution functionClassical logicProbabilistic logicRandom variableŁukasiewicz logicDe Morgan algebraMathematicsProbability measure
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Timed Sets, Functional Complexity, and Computability

2012

AbstractThe construction of various categories of “timed sets” is described in which the timing of maps is considered modulo a “complexity order”. The properties of these categories are developed: under appropriate conditions they form discrete, distributive restriction categories with an iteration. They provide a categorical basis for modeling functional complexity classes and allow the development of computability within these settings. Indeed, by considering “program objects” and the functions they compute, one can obtain models of computability – i.e. Turing categories – in which the total maps belong to specific complexity classes. Two examples of this are introduced in some detail whi…

Discrete mathematicscomplexity measurescomputabilityTheoretical computer scienceGeneral Computer ScienceBasis (linear algebra)Restriction categoriesComputabilityModuloTuring categoriesfunctional complexityTheoretical Computer ScienceDistributive propertyMathematics::Category TheoryComplexity classCategorical variableTuringcomputerPMathematicscomputer.programming_languageComputer Science(all)Electronic Notes in Theoretical Computer Science
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