Search results for "NUMBER"

showing 10 items of 3939 documents

Substitution systems and nonextensive statistics

2015

Abstract Substitution systems evolve in time by generating sequences of symbols from a finite alphabet: At a certain iteration step, the existing symbols are systematically replaced by blocks of N k symbols also within the alphabet (with N k , a natural number, being the length of the k th block of the substitution). The dynamics of these systems leads naturally to fractals and self-similarity. By using B -calculus (Garcia-Morales, 2012) universal maps for deterministic substitution systems both of constant and non-constant length, are formulated in 1D. It is then shown how these systems can be put in direct correspondence with Tsallis entropy. A ‘Second Law of Thermodynamics’ is also prove…

Statistics and ProbabilityDiscrete mathematicsTsallis entropymedia_common.quotation_subjectSymbolic dynamicsBlock (permutation group theory)Substitution (algebra)Natural numberSecond law of thermodynamicsCondensed Matter PhysicsLimit (mathematics)Constant (mathematics)Mathematicsmedia_commonPhysica A: Statistical Mechanics and its Applications
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A spatially filtered mixture of β-convergence regressions for EU regions, 1980–2002

2007

Assessing regional growth and convergence across Europe is a matter of primary relevance. Empirical models that do not account for structural heterogeneities and spatial effects may face serious misspecification problems. In this work, a mixture regression approach is applied to the beta-convergence model, in order to produce an endogenous selection of regional growth patterns. A priori choices, such as North-South or centre-periphery divisions, are avoided. In addition to this, we deal with the spatial dependence existing in the data, applying a local filter to the data. The results indicate that spatial effects matter, and either absolute, conditional, or club convergence, if extended to …

Statistics and ProbabilityEconomics and EconometricsSmall numberEmpirical modellingSample (statistics)Filter (signal processing)Mathematics (miscellaneous)Rate of convergenceConvergence (routing)StatisticsOutlierEconometricsSpatial dependenceSettore SECS-P/01 - Economia PoliticaRegional growth - Convergence patterns - Mixture regression - Spatial effectsSocial Sciences (miscellaneous)MathematicsEmpirical Economics
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Centile estimation for a proportion response variable

2015

This paper introduces two general models for computing centiles when the response variable Y can take values between 0 and 1, inclusive of 0 or 1. The models developed are more flexible alternatives to the beta inflated distribution. The first proposed model employs a flexible four parameter logit skew Student t (logitSST) distribution to model the response variable Y on the unit interval (0, 1), excluding 0 and 1. This model is then extended to the inflated logitSST distribution for Y on the unit interval, including 1. The second model developed in this paper is a generalised Tobit model for Y on the unit interval, including 1. Applying these two models to (1-Y) rather than Y enables model…

Statistics and ProbabilityEstimationDistribution (number theory)EpidemiologyLogitSkew01 natural sciences010104 statistics & probability03 medical and health sciencesVariable (computer science)0302 clinical medicineUnit interval (data transmission)030225 pediatricsStatisticsProbability distributionTobit model0101 mathematicsMathematicsStatistics in Medicine
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Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound?

2011

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (small) constant $\epsilon>0$ multiple of the identity matrix $I$. The lower bound on the eigenvalues of $S_n$ induced by the factor $\epsilon I$ is theoretically convenient, but practically cumbersome, as a good value for the parameter $\epsilon$ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of $S_n$ away …

Statistics and ProbabilityFOS: Computer and information sciencesIdentity matrixMathematics - Statistics TheoryStatistics Theory (math.ST)Upper and lower boundsStatistics - Computation93E3593E15Combinatorics60J27Mathematics::ProbabilityLaw of large numbers65C40 60J27 93E15 93E35stochastic approximationFOS: MathematicsEigenvalues and eigenvectorsComputation (stat.CO)Metropolis algorithmMathematicsProbability (math.PR)Zero (complex analysis)CovariancestabilityUniform continuityBounded function65C40Statistics Probability and Uncertaintyadaptive Markov chain Monte CarloMathematics - Probability
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Identifying Causal Effects with the R Package causaleffect

2017

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately eit…

Statistics and ProbabilityFOS: Computer and information sciencesTheoretical computer sciencecausalityDistribution (number theory)C-componentComputer sciencecausal model02 engineering and technologyCausal structureMethodology (stat.ME)03 medical and health sciences0302 clinical medicinedo-calculusJoint probability distribution0202 electrical engineering electronic engineering information engineering030212 general & internal medicineDAG; do-calculus; causality; causal model; identifiability; graph; C-component; hedge; d-separationlcsh:Statisticslcsh:HA1-4737Statistics - Methodologycomputer.programming_languageCausal modelta112DAGd-separationgraphhedgeidentifiabilityExpression (mathematics)PEARL (programming language)Action (philosophy)kausaliteetti020201 artificial intelligence & image processingStatistics Probability and UncertaintycomputerSoftware
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2021

Abstract We prove the existence of a smoothing for a toroidal crossing space under mild assumptions. By linking log structures with infinitesimal deformations, the result receives a very compact form for normal crossing spaces. The main approach is to study log structures that are incoherent on a subspace of codimension 2 and prove a Hodge–de Rham degeneration theorem for such log spaces that also settles a conjecture by Danilov. We show that the homotopy equivalence between Maurer–Cartan solutions and deformations combined with Batalin–Vilkovisky theory can be used to obtain smoothings. The construction of new Calabi–Yau and Fano manifolds as well as Frobenius manifold structures on moduli…

Statistics and ProbabilityFrobenius manifoldPure mathematicsAlgebra and Number TheoryConjectureHomotopyCodimensionFano planeSpace (mathematics)Moduli spaceMathematics::Algebraic GeometryDiscrete Mathematics and CombinatoricsGeometry and TopologyMathematics::Symplectic GeometryMathematical PhysicsAnalysisSmoothingMathematicsForum of Mathematics, Pi
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Explicit, identical maximum likelihood estimates for some cyclic Gaussian and cyclic Ising models

2017

Cyclic models are a subclass of graphical Markov models with simple, undirected probability graphs that are chordless cycles. In general, all currently known distributions require iterative procedures to obtain maximum likelihood estimates in such cyclic models. For exponential families, the relevant conditional independence constraint for a variable pair is given all remaining variables, and it is captured by vanishing canonical parameters involving this pair. For Gaussian models, the canonical parameter is a concentration, that is, an off-diagonal element in the inverse covariance matrix, while for Ising models, it is a conditional log-linear, two-factor interaction. We give conditions un…

Statistics and ProbabilityGaussianBinary numberMarkov modelCombinatoricsConstraint (information theory)symbols.namesakeExponential familyConditional independencesymbolsApplied mathematicsIsing modelStatistics Probability and UncertaintyVariable (mathematics)MathematicsStat
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The smallest singular value of a shifted $d$-regular random square matrix

2017

We derive a lower bound on the smallest singular value of a random d-regular matrix, that is, the adjacency matrix of a random d-regular directed graph. Specifically, let $$C_1<d< c n/\log ^2 n$$ and let $$\mathcal {M}_{n,d}$$ be the set of all $$n\times n$$ square matrices with 0 / 1 entries, such that each row and each column of every matrix in $$\mathcal {M}_{n,d}$$ has exactly d ones. Let M be a random matrix uniformly distributed on $$\mathcal {M}_{n,d}$$ . Then the smallest singular value $$s_{n} (M)$$ of M is greater than $$n^{-6}$$ with probability at least $$1-C_2\log ^2 d/\sqrt{d}$$ , where c, $$C_1$$ , and $$C_2$$ are absolute positive constants independent of any other parameter…

Statistics and ProbabilityIdentity matrixAdjacency matrices01 natural sciencesSquare matrixCombinatorics010104 statistics & probabilityMatrix (mathematics)Mathematics::Algebraic GeometryFOS: MathematicsMathematics - Combinatorics60B20 15B52 46B06 05C80Adjacency matrix0101 mathematicsCondition numberCondition numberMathematicsRandom graphsRandom graphLittlewood–Offord theorySingularity010102 general mathematicsProbability (math.PR)InvertibilityRegular graphsSingular valueSmallest singular valueAnti-concentrationSingular probabilitySparse matricesCombinatorics (math.CO)Statistics Probability and UncertaintyRandom matricesRandom matrixMathematics - ProbabilityAnalysis
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A generalization of the inhomogeneity measure for point distributions to the case of finite size objects

2008

The statistical measure of spatial inhomogeneity for n points placed in chi cells each of size kxk is generalized to incorporate finite size objects like black pixels for binary patterns of size LxL. As a function of length scale k, the measure is modified in such a way that it relates to the smallest realizable value for each considered scale. To overcome the limitation of pattern partitions to scales with k being integer divisors of L we use a sliding cell-sampling approach. For given patterns, particularly in the case of clusters polydispersed in size, the comparison between the statistical measure and the entropic one reveals differences in detection of the first peak while at other sca…

Statistics and ProbabilityLength scalePlanarStatistical Mechanics (cond-mat.stat-mech)PixelMathematical analysisFOS: Physical sciencesBinary numberGeometryCondensed Matter PhysicsCondensed Matter - Statistical MechanicsUniversality (dynamical systems)MathematicsPhysica A: Statistical Mechanics and its Applications
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Solving type-2 assembly line balancing problem with fuzzy binary linear programming

2013

This paper deals with the use of fuzzy set theory as a viable alternative method for modelling and solving the stochastic assembly line balancing problem. This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 SALBP-2 with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The job processing times are formulated by triangular fuzzy membership functions using their statistical distributions. This study proposes to solve a Fuzzy Binary Linear Problem FBLP with fuzzy coefficients in the objective function and in a constraint. Finally, the effect of the unbalancing of a station in…

Statistics and ProbabilityMathematical optimizationNeuro-fuzzyFuzzy setGeneral EngineeringDefuzzificationFuzzy logicFuzzy transportationArtificial IntelligenceFuzzy set operationsFuzzy numberFuzzy associative matrixAlgorithmMathematicsJournal of Intelligent &amp; Fuzzy Systems
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