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814

Feb 3, 2011
02/11

by
NASA; Langley Research Center

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In this program, NASA engineers and researchers use data analysis and measurement to study the auroras, key regions of the Earth’s geospace or space environment. To license this film and get a higher quality version for broadcast/film purposes, contact A/V Geeks LLC .

Topics: NASA, data analysis, Earth

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441

Feb 3, 2011
02/11

by
NASA; Langley Research Center

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NASA engineers and researchers use data analysis and measurement to predict solar storms, anticipate how they will affect the Earth, and improve our understanding of the Sun-Earth system. To license this film and get a higher quality version for broadcast/film purposes, contact A/V Geeks LLC .

Topics: NASA, data analysis, Sun

"Mining" is the extraction of valuable materials from the core of the earth which are of great economic interest or importance. Traditionally, mining has been used at excavation sites for extraction of minerals like gold and copper. Data mining comprises of unearthing useful patterns from a data warehouse which is the source of integrated data. Data mining can also be used as a BI (Business Intelligence) tool to predict or derive useful patterns by the analysis of current and...

Topics: Data Mining, Data Analysis

Quantitative Data Analysis Using SPSS

Topic: Quantitative Data Analysis Using SPSS

In today’s data-intensive world, the power to analyze huge amounts of data is critical to the success of any organization, including the military. Many data analysis tools have been developed in the past decade along with the high-performance machine learning algorithms. At present, many of these tools unfortunately are out of reach of the target audience—subject matter experts—because one must master some of the advanced computer science concepts to use these tools effectively. This...

Topics: data analysis, machine learning, Spark

In data mining, Cyber Crime management is an interesting application where it plays an important role in handling of crime data. Cyber Crime investigation has very significant role of police system in any country. There had been an enormous increase in the crime in recent years. With rapid popularity of the internet, crime information maintained in web is becoming increasingly rampant. In this paper the data mining techniques are used to analyze the web data. This paper presents detailed...

Topics: Crime data analysis, classification, clustering

609
609

Feb 6, 2011
02/11

by
NASA; Langley Research Center

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In Measurement, Ratios, and Graphing: 3…2…1…Crash!, students will learn the history of the National Aeronautics and Space Administration (NASA) and discover how NASA Langley Research Center improves aircraft performance and safety by conducting extreme tests such as crashing planes, skidding tires, and blasting water. Students will observe NASA engineers using measurement, ratios, and graphing to make predictions and draw conclusions during their extreme tests. Students will learn how...

Topics: NASA, aviation, aeronautics, data analysis

Quantitative Data Analysis Using Spss

Topic: Quantitative Data Analysis Using Spss

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6.0

Jun 30, 2018
06/18

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E. L de Santa Helena; C. M. Nascimento; G. J. L. Gerhardt

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The q-Gaussians are a class of stable distributions which are present in many scientific fields, and that behave as heavy tailed distributions for an especific range of q values. The identification of these values, which are used in the description of systems, is sometimes a hard task. In this work the identification of a q-Gaussian distribution from empirical data was done by a measure of its tail weight using robust statistics. Numerical methods were used to generate artificial data, to find...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.1287

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5.0

Jun 28, 2018
06/18

by
A. B. Kukushkin; P. A. Sdvizhenskii

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A wide class of non-stationary superdiffusive transport on a uniform background with a power-law decay, at large distances, of the step-length probability distribution function (PDF) is shown to possess an automodel solution. The solution for Green function is constructed using the scaling laws for the propagation front (relevant-to-superdiffusion average displacement) and asymptotic solutions far beyond and far in advance of the propagation front. These scaling laws are determined essentially...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1511.08910

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3.0

Jun 28, 2018
06/18

by
Teresa Scholz; Frank Raischel; Vitor V. Lopes; Bernd Lehle; Matthias Wächter; Joachim Peinke; Pedro G. Lind

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This paper presents a direct method to obtain the deterministic and stochastic contribution of the sum of two independent sets of stochastic processes, one of which is composed by Ornstein-Uhlenbeck processes and the other being a general (non-linear) Langevin process. The method is able to distinguish between all stochastic process, retrieving their corresponding stochastic evolution equations. This framework is based on a recent approach for the analysis of multidimensional Langevin-type...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1510.07285

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3.0

Jun 28, 2018
06/18

by
Zihan Huang; Gaoming Wang; Zhao Yu

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We generalize the non-Gaussian parameter, which is utilized to characterize the distinction of dynamics between realistic and Gaussian Brownian diffusions, in k-dimensional Euclidean space.

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1511.06672

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8.0

Jun 30, 2018
06/18

by
Teresa Scholz; Vitor V. Lopes; Pedro Lind; Frank Raischel

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A method is proposed to reconstruct a cyclic time-inhomogeneous Markov pro- cess from measured data. First, a time-inhomogeneous Markov model is fit to the data, taken here from measurements on a wind turbine. From the time-dependent transition matrices, the time-dependent Kramers-Moyal coefficients of the corresponding stochastic process are computed. Further applications of this method are discussed.

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.0203

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8.0

Jun 30, 2018
06/18

by
S. Chen; X. Lan; Y. Hu; Q. Liu; Y. Deng

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Forecasting can estimate the statement of events according to the historical data and it is considerably important in many disciplines. At present, time series models have been utilized to solve forecasting problems in various domains. In general, researchers use curve fitting and parameter estimation methods (moment estimation, maximum likelihood estimation and least square method) to forecast. In this paper, a new sight is given to the forecasting and a completely different method is proposed...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1403.1713

15
15

Jun 27, 2018
06/18

by
Wei Huang; Yu-jian Li; Deyong Kang; Zhi Chen

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The ship-rocking is a crucial factor which affects the accuracy of the ocean-based flight vehicle measurement. Here we have analyzed four groups of ship-rocking time series in horizontal and vertical directions utilizing a Hilbert based method from statistical physics. Our method gives a way to construct an analytic signal on the two-dimensional plane from a one-dimensional time series. The analytic signal share the complete property of the original time series. From the analytic signal of a...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1505.01117

14
14

Jun 26, 2018
06/18

by
Ariel Haimovici; Matteo Marsili

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We discuss a Bayesian model selection approach to high dimensional data in the deep under sampling regime. The data is based on a representation of the possible discrete states $s$, as defined by the observer, and it consists of $M$ observations of the state. This approach shows that, for a given sample size $M$, not all states observed in the sample can be distinguished. Rather, only a partition of the sampled states $s$ can be resolved. Such partition defines an {\em emergent} classification...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1502.00356

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6.0

Jun 29, 2018
06/18

by
Manav Vohra; Xun Huan; Timothy P. Weihs; Omar M. Knio

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Calibration of the uncertain Arrhenius diffusion parameters for quantifying mixing rates in Zr-Al nanolaminate foils was performed in a Bayesian setting [Vohra et al., 2014]. The parameters were inferred in a low temperature regime characterized by homogeneous ignition and a high temperature regime characterized by self-propagating reactions in the multilayers. In this work, we extend the analysis to find optimal experimental designs that would provide the best data for inference. We employ a...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1610.02558

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14

Jun 28, 2018
06/18

by
Carlo Cafaro; Warren M. Lord; Jie Sun; Erik M. Bollt

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Identification of causal structures and quantification of direct information flows in complex systems is a challenging yet important task, with practical applications in many fields. Data generated by dynamical processes or large-scale systems are often symbolized, either because of the finite resolution of the measurement apparatus, or because of the need of statistical estimation. By algorithmic application of causation entropy, we investigated the effects of symbolization on important...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1507.07262

3
3.0

Jun 30, 2018
06/18

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Danuta Makowiec; Beata Graff; Agnieszka Kaczkowska; Grzegorz Graff; Dorota Wejer; Joanna Wdowczyk; Marta Zarczynska-Buchowiecka; Marcin Gruchala; Zbigniew R. Struzik

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Signals from heart transplant recipients can be considered to be a natural source of information for a better understanding of the impact of the autonomic nervous system on the complexity of heart rate variability. Beat-to-beat heart rate variability can be represented as a network of increments between subsequent $RR$-intervals, which makes possible the visualization of short-term heart period fluctuations. A network is constructed of vertices representing increments between subsequent...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.4921

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5.0

Jun 30, 2018
06/18

by
Diego Casadei

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The objective Bayesian treatment of a model representing two independent Poisson processes, labelled as "signal" and "background" and both contributing additively to the total number of counted events, is considered. It is shown that the reference prior for the parameter of interest (the signal intensity) can be well approximated by the widely (ab)used flat prior only when the expected background is very high. On the other hand, a very simple approximation (the limiting form...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.5893

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4.0

Jun 30, 2018
06/18

by
Pedro G. Lind; Matthias Wächter; Joachim Peinke

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We propose a procedure to estimate the fatigue loads on wind turbines, based in a recent framework used for reconstructing data series of stochastic properties measured at wind turbines. Through a standard fatigue analysis, we show that it is possible to accurately estimate fatigue loads in any wind turbine within one wind park, using only the load measurements at one single turbine and the set of wind speed measurements. Our framework consists of deriving a stochastic differential equation...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1410.8005

Mass Spectrometry Data Analysis In Proteomics

Topic: Mass Spectrometry Data Analysis In Proteomics

3
3.0

Jun 30, 2018
06/18

by
Rober Jankowski; Marcin Makowski; Edward W. Piotrowski

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We present a new method of estimating the dispersion of a distribution which is based on the surprising property of a function that measures information processing intensity. It turns out that this function has a maximum at its fixed point. We use a fixed-point equation to estimate the parameter of the distribution that is of interest to us. We illustrate the estimation method by using the example of an exponential distribution. The codes of programs that calculate the experimental values of...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.0262

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6.0

Jun 30, 2018
06/18

by
Gabriele Gradoni; Valter Mariani Primiani; Franco Moglie

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The use of correlation matrices to evaluate the number of uncorrelated stirrer positions of reverberation chamber has widespread applications in electromagnetic compatibility. We present a comparative study of recent techniques based on multivariate correlation functions aimed at relating space-frequency inhomogeneities/anisotropies to the reduction of uncorrelated positions. Full wave finite-difference time domain simulations of an actual reverberation chamber are performed through an in-house...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.6335

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4.0

Jun 29, 2018
06/18

by
Philipp Batz; Andreas Ruttor; Manfred Opper

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We present a method for the nonparametric estimation of the drift function of certain types of stochastic differential equations from the empirical density. It is based on a variational formulation of the Fokker-Planck equation. The minimization of an empirical estimate of the variational functional using kernel based regularization can be performed in closed form. We demonstrate the performance of the method on second order, Langevin-type equations and show how the method can be generalized to...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1603.01159

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15

Jun 27, 2018
06/18

by
Diego Casadei; Cornelius Grunwald; Kevin Kröninger; Florian Mentzel

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Searches for faint signals in counting experiments are often encountered in particle physics and astrophysics, as well as in other fields. Many problems can be reduced to the case of a model with independent and Poisson-distributed signal and background. Often several background contributions are present at the same time, possibly correlated. We provide the analytic solution of the statistical inference problem of estimating the signal in the presence of multiple backgrounds, in the framework...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1504.02566

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4.0

Jun 29, 2018
06/18

by
R. A. Ewings; A. Buts; M. D. Le; J. van Duijn; I. Bustinduy; T. G. Perring

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The HORACE suite of programs has been developed to work with large multiple-measurement data sets collected from time-of-flight neutron spectrometers equipped with arrays of position-sensitive detectors. The software allows exploratory studies of the four dimensions of reciprocal space and excitation energy to be undertaken, enabling multi-dimensional subsets to be visualized, algebraically manipulated, and models for the scattering to simulated or fitted to the data. The software is designed...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.05895

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3.0

Jun 29, 2018
06/18

by
Vladimir A. Mandelshtam

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In a recent paper [Int. J. Quant. Chem. (2016) DOI: 10.1002/qua.25144, arXiv:1502.06579] Markovich, Blau, Sanders, and Aspuru-Guzik presented a numerical evaluation and comparison of three methods, Compressed Sensing (CS), Super-Resolution (SR), and Filter Diagonalization (FDM), on their ability of "recovering information" from time signals, concluding that CS and RS outperform FDM. We argue that this comparison is invalid for the following reasons. FDM is a well established method...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1606.00391

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5.0

Jun 28, 2018
06/18

by
Łukasz Rudnicki; Irene V. Toranzo; Pablo Sanchez-Moreno; Jesus S. Dehesa

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We introduce and discuss the notion of monotonicity for the complexity measures of general probability distributions, patterned after the resource theory of quantum entanglement. Then, we explore whether this property is satisfied by the three main intrinsic measures of complexity (Cramer-Rao, Fisher-Shannon, LMC) and some of their generalizations.

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1510.01547

5
5.0

Jun 30, 2018
06/18

by
Robert W. Johnson

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The algorithm AMGKQ for adaptive multivariate Gauss-Kronrod quadrature over hyper-rectangular regions of arbitrary dimensionality is proposed and implemented in Octave/MATLAB. It can approximate numerically any number of integrals over a common domain simultaneously. Improper integrals are addressed through singularity weakening coordinate transformations. Internal singularities are addressed through the use of breakpoints. Its accuracy performance is investigated thoroughly, and its running...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1410.1064

5
5.0

Jun 29, 2018
06/18

by
Elena Agliari; Flavia Tavani

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We consider the Dyson hierarchical graph $\mathcal{G}$, that is a weighted fully-connected graph, where the pattern of weights is ruled by the parameter $\sigma \in (1/2, 1]$. Exploiting the deterministic recursivity through which $\mathcal{G}$ is built, we are able to derive explicitly the whole set of the eigenvalues and the eigenvectors for its Laplacian matrix. Given that the Laplacian operator is intrinsically implied in the analysis of dynamic processes (e.g., random walks) occurring on...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.05864

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3.0

Jun 29, 2018
06/18

by
Benjamin B Schroeder; Sean T Smith; Philip J Smith; Thomas H Fletcher; Andrew Packard; Michael Frenklach; Arun Hegde; Wenyu Li; James Oreluk

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When performing large-scale, high-performance computations of multi-physics applications, it is common to limit the complexity of physics sub-models comprising the simulation. For a hierarchical system of coal boiler simulations a scale-bridging model is constructed to capture characteristics appropriate for the application-scale from a detailed coal devolatilization model. Such scale-bridging allows full descriptions of scale-applicable physics, while functioning at reasonable computational...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1609.00871

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4.0

Jun 29, 2018
06/18

by
Chen-Yun Lin; Arin Minasian; Xin Jessica Qi; Hau-Tieng Wu

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We extensively study the rotational group structure inside the patch space by introducing the fiber bundle structure. The rotational group structure leads to a new image denoising algorithm called the \textit{vector non-local Euclidean median} (VNLEM). The theoretical aspect of VNLEM is studied, which explains why the VNLEM and traditional non-local mean/non-local Euclidean median (NLEM) algorithm work. The numerical issue of the VNLEM is improved by taking the orientation feature in the...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1611.05073

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3.0

Jun 29, 2018
06/18

by
Miriam Lucio Martínez; Diego Martínez Santos; Francesco Dettori

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In high energy physics, results from searches for new particles or rare processes are often reported using a modified frequentist approach, known as $\rm{CL_s}$ method. In this paper, we study the properties of the derivatives of $\rm{CL_s}$ and $\rm{CL_{s+b}}$ as signal strength estimators if the confidence levels are interpreted as credible intervals. Our approach allows obtaining best fit points and $\chi^2$ functions which can be used for phenomenology studies. In addition, this approach...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1611.06293

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4.0

Jun 30, 2018
06/18

by
Bernard Lacaze

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In a communication scheme, there exist points at the transmitter and at the receiver where the wave is reduced to a finite set of functions of time which describe amplitudes and phases. For instance, the information is summarized in electrical cables which preceed or follow antennas. In many cases, a random propagation time is sufficient to explain changes induced by the medium. In this paper we study models based on stable probability laws which explain power spectra due to propagation of...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1411.5249

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3.0

Jun 30, 2018
06/18

by
Zhizhen Zhao; Dimitrios Giannakis

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Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1412.3831

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3.0

Jun 29, 2018
06/18

by
Dimitrios Giannakis; Matina Gkioulidou; John Harlim

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We propose a nonparametric approach for probabilistic prediction of the AL index trained with AL and solar wind ($v B_z$) data. Our framework relies on the diffusion forecasting technique, which views AL and $ v B_z $ data as observables of an autonomous, ergodic, stochastic dynamical system operating on a manifold. Diffusion forecasting builds a data-driven representation of the Markov semigroup governing the evolution of probability measures of the dynamical system. In particular, the Markov...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1612.07272

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4.0

Jun 29, 2018
06/18

by
Susmita Bhaduri; Dipak Ghosh

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In this paper, we study the fractality of void probability distribution measured in $^{32}$S-Ag/Br interaction at an incident energy of $200$ GeV per nucleon. A radically different and rigorous method called \textit{Visibility Graph} analysis is used. This method is shown to reveal a strong scaling character of void probability distribution in all pseurorapidity regions. The scaling exponent, called the Power of the Scale-freeness in Visibility Graph(PSVG), a quantitative parameter related to...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1606.00590

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19

Jun 29, 2018
06/18

by
Harrison B. Prosper

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These lectures introduce the basic ideas and practices of statistical analysis for particle physicists, using a real-world example to illustrate how the abstractions on which statistics is based are translated into practical application.

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1608.03201

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5.0

Jun 29, 2018
06/18

by
György Steinbrecher; Giorgio Sonnino

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In many applications, the probability density function is subject to experimental errors. In this work the continuos dependence of a class of generalized entropies on the experimental errors is studied. This class includes the C. Shannon, C. Tsallis, A. R\'{e}nyi and generalized R\'{e}nyi entropies. By using the connection between R\'{e}nyi or Tsallis entropies, and the \textit{distance} in a the Lebesgue functional spaces, we introduce a further extensive generalizations of the R\'{e}nyi...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1603.06240

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21

Jun 26, 2018
06/18

by
X. San Liang

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Recently, a rigorous yet concise formula has been derived to evaluate the information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing three types of fundamental mechanisms that govern the marginal entropy change of the flow recipient. A normalized or relative flow measures its importance relative to other mechanisms. In analyzing...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1501.03548

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15

Jun 27, 2018
06/18

by
S. M. Abrarov; B. M. Quine

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We present a rational approximation for rapid and accurate computation of the Voigt function, obtained by residue calculus. The computational test reveals that with only $16$ summation terms this approximation provides average accuracy ${10^{- 14}}$ over a wide domain of practical interest $0 < x < 40,000$ and ${10^{- 4}} < y < {10^2}$ for applications using the HITRAN molecular spectroscopic database. The proposed rational approximation takes less than half the computation time of...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1504.00322

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5.0

Jun 28, 2018
06/18

by
B. Kaulakys; M. Alaburda; J. Ruseckas

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The origin of the low-frequency noise with power spectrum $1/f^\beta$ (also known as $1/f$ fluctuations or flicker noise) remains a challenge. Recently, the nonlinear stochastic differential equations for modeling $1/f^\beta$ noise have been proposed and analyzed. Here we use the self-similarity properties of this model with respect to the nonlinear transformations of the variable of these equations and show that $1/f^\beta$ noise of the observable may yield from the power-law transformations...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1512.04298

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6.0

Jun 29, 2018
06/18

by
Anna Carbone; Ken Kiyono

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The Detrending Moving Average (DMA) algorithm has been widely used in its several variants for characterizing long-range correlations of random signals and sets (one-dimensional sequences or high-dimensional arrays) either over time or space. In this paper, mainly based on analytical arguments, the scaling performances of the centered DMA, including higher-order ones, are investigated by means of a continuous time approximation and a frequency response approach. Our results are also confirmed...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1602.01260

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10.0

Jun 27, 2018
06/18

by
F. Vernotte; M. Lenczner; P. -Y. Bourgeois; E. Rubiola

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This article introduces the Parabolic Variance (PVAR), a wavelet variance similar to the Allan variance, based on the Linear Regression (LR) of phase data. The companion article arXiv:1506.05009 [physics.ins-det] details the $\Omega$ frequency counter, which implements the LR estimate. The PVAR combines the advantages of AVAR and MVAR. PVAR is good for long-term analysis because the wavelet spans over $2 \tau$, the same of the AVAR wavelet; and good for short-term analysis because the response...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1506.00687

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14

Jun 28, 2018
06/18

by
Shintaro Mori; Masafumi Hino; Masato Hisakado; Taiki Takahashi

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We propose a method of detecting non-self-correcting information cascades in experiments in which subjects choose an option sequentially by observing the choices of previous subjects. The method uses the correlation function $C(t)$ between the first and the $t+1$-th subject's choices. $C(t)$ measures the strength of the domino effect, and the limit value $c\equiv \lim_{t\to \infty}C(t)$ determines whether the domino effect lasts forever $(c>0)$ or not $(c=0)$. The condition $c>0$ is an...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1507.07265

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8.0

Jun 30, 2018
06/18

by
Wagner S. de Lima; Emerson L. de Santa Helena

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q-Gaussian distribution appear in many science areas where we can find systems that could be described within a nonextensive framework. Usually, a way to assert that these systems belongs to nonextensive framework is by means of numerical data analysis. To this end, we implement random number generator for q-Gaussian distribution, while we present how to computing its probability density function, cumulative density function and quantile function besides a tail weight measurement using robust...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.06172

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9.0

Jun 27, 2018
06/18

by
Sergio Davis

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It is shown that a consistent application of Bayesian updating from a prior probability density to a posterior using evidence in the form of expectation constraints leads to exactly the same results as the application of the maximum entropy principle, namely a posterior belonging to the exponential family. The Bayesian updating procedure presented in this work is not expressed as a variational principle, and does not involve the concept of entropy. Therefore it conceptually constitutes a...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1503.03451

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9.0

Jun 29, 2018
06/18

by
Luca Lista

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A simple computer-based algorithm has been developed to identify pre-modern coins minted from the same dies, intending mainly coins minted by hand-made dies designed to be applicable to images taken from auction websites or catalogs. Though the method is not intended to perform a complete automatic classification, which would require more complex and intensive algorithms accessible to experts of computer vision its simplicity of use and lack of specific requirement about the quality of pictures...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.04074

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Jun 30, 2018
06/18

by
Gaurav Bhole; Abhishek Shukla; T. S. Mahesh

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Benford's law is a statistical inference to predict the frequency of significant digits in naturally occurring numerical databases. In such databases this law predicts a higher occurrence of the digit 1 in the most significant place and decreasing occurrences to other larger digits. Although counter-intuitive at first sight, Benford's law has seen applications in a wide variety of fields like physics, earth-science, biology, finance etc. In this work, we have explored the use of Benford's law...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1408.5735