Search results for "65"

showing 10 items of 1111 documents

CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration

2017

International audience; In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor a…

FOS: Computer and information sciencesInverse problemsMathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer Vision and Pattern Recognition (cs.CV)General MathematicsComputer Science - Computer Vision and Pattern RecognitionMachine Learning (stat.ML)Mathematics - Statistics TheoryImage processingStatistics Theory (math.ST)02 engineering and technologyDebiasing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciencesRegularization (mathematics)Boosting010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Variational methods[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Statistics - Machine LearningRefittingMSC: 49N45 65K10 68U10[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingFOS: Mathematics0202 electrical engineering electronic engineering information engineeringCovariant transformation[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsImage restoration[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]MathematicsApplied Mathematics[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]EstimatorInverse problem[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Jacobian matrix and determinantsymbolsTwicing020201 artificial intelligence & image processingAffine transformationAlgorithm
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Unbiased Estimators and Multilevel Monte Carlo

2018

Multilevel Monte Carlo (MLMC) and unbiased estimators recently proposed by McLeish (Monte Carlo Methods Appl., 2011) and Rhee and Glynn (Oper. Res., 2015) are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction…

FOS: Computer and information sciencesMonte Carlo methodWord error rate010103 numerical & computational mathematicsstochastic differential equationManagement Science and Operations ResearchStatistics - Computation01 natural sciences010104 statistics & probabilityStochastic differential equationstratificationSquare rootFOS: MathematicsApplied mathematics0101 mathematicsComputation (stat.CO)stokastiset prosessitMathematicsProbability (math.PR)ta111EstimatorVariance (accounting)unbiased estimatorsComputer Science ApplicationsMonte Carlo -menetelmät65C05 (Primary) 65C30 (Secondary)efficiencykerrostuneisuusVariance reductionunbiasemultilevel Monte CarlodifferentiaaliyhtälötMathematics - ProbabilityOperations Research
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Mahonian STAT on words

2016

In 2000, Babson and Steingrimsson introduced the notion of what is now known as a permutation vincular pattern, and based on it they re-defined known Mahonian statistics and introduced new ones, proving or conjecturing their Mahonity. These conjectures were proved by Foata and Zeilberger in 2001, and by Foata and Randrianarivony in 2006.In 2010, Burstein refined some of these results by giving a bijection between permutations with a fixed value for the major index and those with the same value for STAT , where STAT is one of the statistics defined and proved to be Mahonian in the 2000 Babson and Steingrimsson's paper. Several other statistics are preserved as well by Burstein's bijection.At…

FOS: Computer and information sciencesQA75[ INFO ] Computer Science [cs]Discrete Mathematics (cs.DM)Major index0102 computer and information sciencesMathematical Analysis01 natural sciencesWords and PermutationsCombinatorial problemsEquidistributionTheoretical Computer ScienceCombinatoricssymbols.namesakePermutationBijectionsFOS: MathematicsMathematics - CombinatoricsMathematical proofs[INFO]Computer Science [cs]0101 mathematicsStatisticMathematicsStatisticZ665Algebraic combinatoricsMathematics::CombinatoricsFormal power seriesPatternPermutationsEulerian path16. Peace & justiceComputer Science Applications010101 applied mathematics010201 computation theory & mathematicsCombinatoricsSignal ProcessingsymbolsBijectionCombinatorics (math.CO)Information SystemsComputer Science - Discrete Mathematics
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

2021

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…

FOS: Computer and information sciencesStatistics and ProbabilityComputer Science - Machine LearningcausalityComputer Science - Artificial IntelligenceHeuristic (computer science)Computer scienceeducationMachine Learning (stat.ML)transportabilitycomputer.software_genre01 natural sciencesMachine Learning (cs.LG)R-kielimissing dataQA76.75-76.765; QA273-280010104 statistics & probabilitydo-calculuscausality; do-calculus; selection bias; transportability; missing data; case-control design; meta-analysisStatistics - Machine LearningSearch algorithmselection bias0101 mathematicsParametric statisticspäättelymeta-analyysicase-control designhakualgoritmit113 Computer and information sciencesMissing datameta-analysisIdentification (information)Artificial Intelligence (cs.AI)Causal inferencekausaliteettiIdentifiabilityProbability distributionData miningStatistics Probability and UncertaintycomputerSoftwareJournal of Statistical Software
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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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The infinite dihedral group

2022

We describe the infinite dihedral group as automaton group. We collect basic results and give full proofs in details for all statements.

FOS: Mathematics20F65 (Primary) 05C25 20E08 68Q70 13F25 (Secondary)Computer Science::Symbolic ComputationGroup Theory (math.GR)Nonlinear Sciences::Cellular Automata and Lattice GasesMathematics - Group TheoryComputer Science::Formal Languages and Automata Theory
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Functional A Posteriori Error Equalities for Conforming Mixed Approximations of Elliptic Problems

2014

In this paper we show how to find the exact error (not just an estimate of the error) of a conforming mixed approximation by using the functional type a posteriori error estimates in the spirit of Repin. The error is measured in a mixed norm which takes into account both the primal and dual variables. We derive this result for elliptic partial differential equations of a certain class. We first derive a special version of our main result by using a simplified reaction-diffusion problem to demonstrate the strong connection to the classical functional a posteriori error estimates of Repin. After this we derive the main result in an abstract setting. Our main result states that in order to obt…

FOS: MathematicsMathematics - Numerical AnalysisNumerical Analysis (math.NA)65N15
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Worst case approach in convex minimization problems with uncertain data

2015

This paper concerns quantitative analysis of errors generated by incompletely known data in convex minimization problems. The problems are discussed in the mixed setting and the duality gap is used as the fundamental error measure. The influence of the indeterminate data is measured using the worst case scenario approach. The worst case error is decomposed into two computable quantities, which allows the quantitative comparison between errors resulting from the inaccuracy of the approximation and the data uncertainty. The proposed approach is demonstrated on a paradigm of a nonlinear reaction-diffusion problem together with numerical examples.

FOS: MathematicsNumerical Analysis (math.NA)Mathematics - Numerical Analysis65N15 49N30 49N15
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Search for Fingerprints of Tetrahedral Symmetry in $^{156}Gd$

2008

Theoretical predictions suggest the presence of tetrahedral symmetry as an explanation for the vanishing intra-band E2-transitions at the bottom of the odd-spin negative parity band in $^{156}Gd$. The present study reports on experiment performed to address this phenomenon. It allowed to determine the intra-band E2 transitions and branching ratios B(E2)/B(E1) of two of the negative-parity bands in $^{156}Gd$.

FOS: Physical sciences23.20.En 21.65.-f 21.10.Ma 21.60.-n[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Nuclear Experiment (nucl-ex)Nuclear Experiment
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La influencia del distrito industrial y el subsector en la actividad empresarial. Una aplicación al sector textil-confección.

2007

RESUMEN El objetivo de esta investigación es examinar para una industria concreta como es el textil-confección, la influencia de la localización y actividad productiva de las empresas en las percepciones directivas y en la internacionalización. Para alcanzar el objetivo propuesto, este trabajo se ha articulado en torno a cinco capítulos. Los tres primeros, de carácter más teórico, revisan las principales aportaciones relativas al fenómeno de las redes, los distritos industriales y la internacionalización; asimismo, sirven para contextualizar el sector textil-confección. Además, en un apartado final de cada uno de estos capítulos se han resumido las principales conclusiones y se han especifi…

Facultat d'Economianone65
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