Search results for "FOS: Mathematics"

showing 10 items of 1448 documents

Open and Closed Prefixes of Sturmian Words

2013

A word is closed if it contains a proper factor that occurs both as a prefix and as a suffix but does not have internal occurrences, otherwise it is open. We deal with the sequence of open and closed prefixes of Sturmian words and prove that this sequence characterizes every finite or infinite Sturmian word up to isomorphisms of the alphabet. We then characterize the combinatorial structure of the sequence of open and closed prefixes of standard Sturmian words. We prove that every standard Sturmian word, after swapping its first letter, can be written as an infinite product of squares of reversed standard words.

FOS: Computer and information sciencesSequenceFibonacci numberDiscrete Mathematics (cs.DM)Formal Languages and Automata Theory (cs.FL)Sturmian wordStructure (category theory)Sturmian wordInfinite productComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Computer Science - Formal Languages and Automata Theory68R15CombinatoricsPrefixComputer Science::Discrete MathematicsCombinatorics on words Sturmian wordFOS: MathematicsMathematics - CombinatoricsClosed wordsCombinatorics (math.CO)SuffixWord (group theory)Computer Science::Formal Languages and Automata TheoryMathematicsComputer Science - Discrete Mathematics
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Generalized Logical Operations among Conditional Events

2018

We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of n conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional random quantities. We define the notion of negation, by verifying De Morgan’s Laws. We also show that conjunction and disjunction satisfy the associative and commutative properties, and a monotonicity property. Then, we give some results on coherence of prevision assessments for some families of compounded conditionals; in particular we examine the Frechet-Hoeffding bounds. Moreover, we study the reverse probabilistic inference from the conjunction $\mathcal…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaComputer Science - Artificial IntelligenceComputer scienceMonotonic functionProbabilistic reasoning02 engineering and technologyCommutative Algebra (math.AC)Conditional random quantitieFréchet-Hoeffding boundCoherent extensionNegationArtificial IntelligenceQuasi conjunction0202 electrical engineering electronic engineering information engineeringFOS: MathematicsCoherent prevision assessmentConditional eventNon-monotonic logicRule of inferenceCommutative propertyAssociative propertyDiscrete mathematicsProbability (math.PR)Probabilistic logicOrder (ring theory)ConjunctionMathematics - LogicCoherence (philosophical gambling strategy)p-entailmentProbabilistic inferenceMathematics - Commutative AlgebraConjunction (grammar)Artificial Intelligence (cs.AI)020201 artificial intelligence & image processingInference ruleNegationLogic (math.LO)Mathematics - ProbabilityDisjunction
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Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects

2013

We make a probabilistic analysis related to some inference rules which play an important role in nonmonotonic reasoning. In a coherence-based setting, we study the extensions of a probability assessment defined on $n$ conditional events to their quasi conjunction, and by exploiting duality, to their quasi disjunction. The lower and upper bounds coincide with some well known t-norms and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are rules for which any finite family of conditional events p-entails the associated quasi conjunction and quasi disjunction. We examine some cases of logical de…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaInformation Systems and ManagementComputer Science - Artificial Intelligencet-Norms/conormDuality (mathematics)goodman-nguyen inclusion relation; lower/upper probability bounds; t-norms/conorms; generalized loop rule; coherence; quasi conjunction/disjunctionComputer Science::Artificial IntelligenceTheoretical Computer ScienceArtificial IntelligenceFOS: MathematicsProbabilistic analysis of algorithmsNon-monotonic logicRule of inferenceLower/upper probability boundGoodman–Nguyen inclusion relationMathematicsEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniDiscrete mathematicsInterpretation (logic)Probability (math.PR)Probabilistic logicCoherence (philosophical gambling strategy)Generalized Loop ruleComputer Science ApplicationsAlgebraArtificial Intelligence (cs.AI)Control and Systems EngineeringQuasi conjunction/disjunctionCoherenceMathematics - ProbabilitySoftwareInformation Sciences
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Pattern Recovery in Penalized and Thresholded Estimation and its Geometry

2023

We consider the framework of penalized estimation where the penalty term is given by a real-valued polyhedral gauge, which encompasses methods such as LASSO (and many variants thereof such as the generalized LASSO), SLOPE, OSCAR, PACS and others. Each of these estimators can uncover a different structure or ``pattern'' of the unknown parameter vector. We define a general notion of patterns based on subdifferentials and formalize an approach to measure their complexity. For pattern recovery, we provide a minimal condition for a particular pattern to be detected by the procedure with positive probability, the so-called accessibility condition. Using our approach, we also introduce the stronge…

FOS: Computer and information sciencesStatistics - Machine LearningFOS: MathematicsMathematics - Statistics TheoryMachine Learning (stat.ML)[MATH] Mathematics [math]Statistics Theory (math.ST)
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Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model

2020

In this paper, we analyse classical variants of the Spectral Clustering (SC) algorithm in the Dynamic Stochastic Block Model (DSBM). Existing results show that, in the relatively sparse case where the expected degree grows logarithmically with the number of nodes, guarantees in the static case can be extended to the dynamic case and yield improved error bounds when the DSBM is sufficiently smooth in time, that is, the communities do not change too much between two time steps. We improve over these results by drawing a new link between the sparsity and the smoothness of the DSBM: the more regular the DSBM is, the more sparse it can be, while still guaranteeing consistent recovery. In particu…

FOS: Computer and information sciencesStatistics and ProbabilityComputer Science - Machine Learning[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Statistics - Machine LearningFOS: MathematicsMachine Learning (stat.ML)Mathematics - Statistics TheoryStatistics Theory (math.ST)Statistics Probability and Uncertainty[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Machine Learning (cs.LG)
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Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions

2021

We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation techniques. Instead, our method uses standard time-discretised approximations of diffusions, such as the Euler--Maruyama scheme. Our approach is based on particle marginal Metropolis--Hastings, a particle filter, randomised multilevel Monte Carlo, and importance sampling type correction of approximate Markov chain Monte Carlo. The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases. We give conver…

FOS: Computer and information sciencesStatistics and ProbabilityDiscretizationComputer scienceMarkovin ketjutInference010103 numerical & computational mathematicssequential Monte CarloBayesian inferenceStatistics - Computation01 natural sciencesMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakediffuusio (fysikaaliset ilmiöt)FOS: MathematicsDiscrete Mathematics and Combinatorics0101 mathematicsHidden Markov modelComputation (stat.CO)Statistics - Methodologymatematiikkabayesilainen menetelmäApplied MathematicsProbability (math.PR)diffusionmatemaattiset menetelmätMarkov chain Monte CarloMarkov chain Monte CarloMonte Carlo -menetelmätNoiseimportance sampling65C05 (primary) 60H35 65C35 65C40 (secondary)Modeling and Simulationsymbolsmatemaattiset mallitStatistics Probability and Uncertaintymultilevel Monte CarloParticle filterAlgorithmMathematics - ProbabilityImportance samplingSIAM/ASA Journal on Uncertainty Quantification
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Thresholding projection estimators in functional linear models

2008

We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits to get easily mean squared error of prediction as well as estimators of the derivatives of the regression function. We prove these estimators are minimax and rates of convergence are given for some particular cases.

FOS: Computer and information sciencesStatistics and ProbabilityMathematical optimizationStatistics::TheoryMean squared error of predictionMean squared errorMathematics - Statistics TheoryStatistics Theory (math.ST)Projection (linear algebra)Methodology (stat.ME)FOS: MathematicsApplied mathematicsStatistics - MethodologyMathematicsLinear inverse problemNumerical AnalysisLinear modelEstimatorRegression analysisMinimaxSobolev spaceThresholdingOptimal rate of convergenceDerivatives estimationRate of convergenceHilbert scaleStatistics Probability and UncertaintyGalerkin methodJournal of Multivariate Analysis
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Asymptotic and bootstrap tests for subspace dimension

2022

Most linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices, see e.g. Ye and Weiss (2003), Tyler et al. (2009), Bura and Yang (2011), Liski et al. (2014) and Luo and Li (2016). The eigen-decomposition of one scatter matrix with respect to another is then often used to determine the dimension of the signal subspace and to separate signal and noise parts of the data. Three popular dimension reduction methods, namely principal component analysis (PCA), fourth order blind identification (FOBI) and sliced inverse regression (SIR) are considered in detail and the first two moments of subsets of the eigenvalues are used to test…

FOS: Computer and information sciencesStatistics and ProbabilityPrincipal component analysisMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMethodology (stat.ME)010104 statistics & probabilityDimension (vector space)Scatter matrixSliced inverse regression0502 economics and businessFOS: MathematicsSliced inverse regressionApplied mathematics0101 mathematicsEigenvalues and eigenvectorsStatistics - Methodology050205 econometrics MathematicsestimointiNumerical AnalysisOrder determinationDimensionality reduction05 social sciencesriippumattomien komponenttien analyysimonimuuttujamenetelmätPrincipal component analysisStatistics Probability and UncertaintySubspace topologySignal subspace
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Completely independent spanning trees in some regular graphs

2014

International audience; Let k >= 2 be an integer and T-1,..., T-k be spanning trees of a graph G. If for any pair of vertices {u, v} of V(G), the paths between u and v in every T-i, 1 <= i <= k, do not contain common edges and common vertices, except the vertices u and v, then T1,... Tk are completely independent spanning trees in G. For 2k-regular graphs which are 2k-connected, such as the Cartesian product of a complete graph of order 2k-1 and a cycle, and some Cartesian products of three cycles (for k = 3), the maximum number of completely independent spanning trees contained in these graphs is determined and it turns out that this maximum is not always k. (C) 2016 Elsevier B.V. All righ…

FOS: Computer and information sciences[ MATH ] Mathematics [math]Discrete Mathematics (cs.DM)Small Depths0102 computer and information sciences02 engineering and technology[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]01 natural sciencesCombinatoricssymbols.namesakeCompletely independent spanning treeFOS: Mathematics0202 electrical engineering electronic engineering information engineeringCartesian productDiscrete Mathematics and CombinatoricsMathematics - Combinatorics[MATH]Mathematics [math]MathematicsConstructionSpanning treeSpanning treeApplied MathematicsComplete graph020206 networking & telecommunications[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Cartesian productIndependent spanning treesGraphPlanar graphPlanar Graphs010201 computation theory & mathematicssymbolsCompletely independent spanning tree.Combinatorics (math.CO)Computer Science - Discrete Mathematics
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Grand Dyck paths with air pockets

2022

Grand Dyck paths with air pockets (GDAP) are a generalization of Dyck paths with air pockets by allowing them to go below the $x$-axis. We present enumerative results on GDAP (or their prefixes) subject to various restrictions such as maximal/minimal height, ordinate of the last point and particular first return decomposition. In some special cases we give bijections with other known combinatorial classes.

FOS: Computer and information sciences[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO][INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]Discrete Mathematics (cs.DM)FOS: MathematicsMathematics - CombinatoricsCombinatorics (math.CO)Computer Science - Discrete Mathematics
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