Academic literature on the topic 'Entropy (statistics)'

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Journal articles on the topic "Entropy (statistics)"

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Esteban, M. D., D. Morales, L. Pardo, and M. L. Menéndez. "Order statistics and $(r,s)$-entropy measures." Applications of Mathematics 39, no. 5 (1994): 321–37. http://dx.doi.org/10.21136/am.1994.134262.

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Thapliyal, Richa, and H. C. Taneja. "Order Statistics Based Measure of Past Entropy." Mathematical Journal of Interdisciplinary Sciences 1, no. 2 (March 2, 2013): 63–70. http://dx.doi.org/10.15415/mjis.2013.12013.

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Thapliyal, Richa, and H. C. Taneja. "Generalized Entropy of Order Statistics." Applied Mathematics 03, no. 12 (2012): 1977–82. http://dx.doi.org/10.4236/am.2012.312272.

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Baratpour, S., J. Ahmadi, and N. R. Arghami. "Entropy properties of record statistics." Statistical Papers 48, no. 2 (April 2007): 197–213. http://dx.doi.org/10.1007/s00362-006-0326-7.

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ZHAO, HUI-HUA, GUANG-LIANG LI, REN ZHAO, MENG-SEN MA, and LI-CHUN ZHANG. "ENTANGLEMENT ENTROPY OF d-DIMENSIONAL BLACK HOLE AND QUANTUM ISOLATED HORIZON." Modern Physics Letters A 28, no. 32 (October 6, 2013): 1350129. http://dx.doi.org/10.1142/s0217732313501290.

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Based on the works of Ghosh et al. who view the black hole entropy as the logarithm of the number of quantum states on the Quantum Isolated Horizon (QIH), the entropy of d-dimensional black hole is studied. According to the Unruh–Verlinde temperature deduced from the concept of entropic force, the statistical entropy of quantum fields under the background of d-dimensional spacetime is calculated by means of quantum statistics. The results reveal the relation between the entanglement entropy of black hole and the logarithm of the number of quantum states and display the effects of dimensions on the correction terms of the entanglement entropy.
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Sangun Park. "The entropy of consecutive order statistics." IEEE Transactions on Information Theory 41, no. 6 (1995): 2003–7. http://dx.doi.org/10.1109/18.476325.

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Parvan, A. S. "Nonextensive statistics of Landsberg-Vedral entropy." EPJ Web of Conferences 204 (2019): 03005. http://dx.doi.org/10.1051/epjconf/201920403005.

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The general formalism for the nonextensive statistics based on the Landsberg-Vedral parametric entropy in the framework of the microcanonical, canonical and grand canonical ensembles was derived. The formulas for the first law of thermodynamics and the thermodynamic quantities in the terms of ensemble averages were obtained in a general form. It was found that under the transformation q → 2 – q the probabilities of microstates of the nonextensive statistics based on the Landsberg-Vedral entropy with the standard expectation values formally resemble the corresponding probabilities of the Tsallis statistics with the generalized expectation values.
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Clark, Daniel E. "Local Entropy Statistics for Point Processes." IEEE Transactions on Information Theory 66, no. 2 (February 2020): 1155–63. http://dx.doi.org/10.1109/tit.2019.2941213.

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Esteban, M. D. "A general class of entropy statistics." Applications of Mathematics 42, no. 3 (June 1997): 161–69. http://dx.doi.org/10.1023/a:1022447020419.

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Abbasnejad, M., and N. R. Arghami. "Renyi Entropy Properties of Order Statistics." Communications in Statistics - Theory and Methods 40, no. 1 (September 22, 2010): 40–52. http://dx.doi.org/10.1080/03610920903353683.

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Dissertations / Theses on the topic "Entropy (statistics)"

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Asaad-Sultan, Asaad M. Abu. "Entropic vector optimization and simulated entropy : theory and applications." Thesis, University of Liverpool, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293838.

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Monteiro, André Bosque. "Entropy statistics and aeronautical evolution." Instituto Tecnológico de Aeronáutica, 2006. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2169.

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Employing the probabilistic entropy methodology, one of the objectives of this work is to develop a robust and simple tool to be employed in the empirical analysis of airplane configuration and technology evolution. Two specific analyses were performed to validate the procedure. One is the evolution of civil aviation transportation in the jet age (1950-2006) and the other being the evolution of fighter aircraft during the period of 1914 to 2009. An extensive study was carried out in order to select the variables used to describe each aircraft. After the creation of the aircraft databank, the tool developed in the present work takes the variables as input do evaluate two important technological indexes: the convergence and the diffusion. Studies analyzing the combination of the diffusion and the convergence indexes, as well as the critical transition of the airplanes were conducted in this paper. The methodology herein developed can be highly helpful as a decision making tool to be used during the conceptual design phase of an airplane.
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Carvalho, André Izecson de. "A design method based in entropy statistics." Instituto Tecnológico de Aeronáutica, 2008. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1169.

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Desde o início da história da aviação, cada nova aeronave é criada para que seja mais econômica, mais rápida, mais leve, melhor do que as que a antecederam. Compreender a evolução tecnológica da aviação é extremamente útil quando se deseja projetar uma nova aeronave. Saviotti (1984) e, posteriormente, Frenken (1997) propuseram um método de análise da evolução tecnológica de aeronaves. Esse método, baseado em conceitos de teoria da informação desenvolvidos por Shannon (1948), especialmente o conceito de entropia estatística, se mostrou bastante eficaz. O método, porém, é essencialmente uma ferramenta de análise, e não de projeto. Baseado em um banco de dados de aeronaves, o método é capaz de determinar em que medida cada uma delas foi influenciada por seus predecessores (o que se denomina "convergência") e, por sua vez, influenciou seus sucessores (o que se denomina "difusão"). Neste trabalho, uma ferramenta de auxílio ao projeto de aeronaves é proposta. Essa ferramenta se baseia no método de entropia estatística. Dadas especificações da aeronave que se deseja projetar, e um banco de dados com informações de diversas aeronaves, é realizada a minimização da entropia do sistema, o que conduz a uma aeronave com alto índice de convergência, ou seja, que tenha absorvido o mais possível da tecnologia de aeronaves existentes. A minimização da entropia é realizada através de um algoritmo genético. O algoritmo foi selecionado devido a sua robustez ao lidar com grandes quantidades de informação, minimizando diversas variáveis independentes simultaneamente mesmo na ausência de uma modelagem física do sistema. Foram realizadas diversas análises para avaliar a eficácia do método da entropia estatística. Em especial, um design criado pelo método foi comparado com três outros projetos com as mesmas especificações, realizados por times distintos de engenheiros utilizando-se de métodos convencionais. Além disso, foi avaliada a gama de especificações na qual o método é eficaz, e os seus limites. Como forma de avaliar mais completamente a qualidade dos resultados produzidos pelo método, estes foram testados, através de uma análise de desempenho das aeronaves obtidas, para avaliar se eram internamente consistentes.
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Weis, Stephan Wilhelm. "Exponential families with incompatible statistics and their entropy distance." kostenfrei, 2009. http://d-nb.info/1000055337/34.

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Taghavianfar, Mohsen. "An Investigation on Network Entropy-Gossiping Protocol and Anti-entropy Evaluation." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2684.

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This thesis is concerned with studying the behavior of a gossiping protocol in the specific sense meant by Ericsson; in the following pages I’ll introduce a Markov process which models the spread of information in such systems. The results will be verified by means of a discreet-event simulation.
Gossiping Protocols, are inherently random in behavior.Nonetheless, they are not structure-less. Their asymptotic behavior when implemented in large scales is the matter of focus in this thesis.
Tel: +46709700505 Address: Pinnharvsgatan 3 E lgh 1202 43147 Mölndal Sweden
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Peccarelli, Adric M. "A Comparison of Variance and Renyi's Entropy with Application to Machine Learning." Thesis, Northern Illinois University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10603911.

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This research explores parametric and nonparametric similarities and disagreements between variance and the information theoretic measure of entropy, specifically Renyi’s entropy. A history and known relationships of the two different uncertainty measures is examined. Then, twenty discrete and continuous parametric families are tabulated with their respective variance and Renyi entropy functions ordered to understand the behavior of these two measures of uncertainty. Finally, an algorithm for variable selection using Renyi’s Quadratic Entropy and its kernel estimation is explored and compared to other popular selection methods using real data.

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Källberg, David. "Nonparametric Statistical Inference for Entropy-type Functionals." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-79976.

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In this thesis, we study statistical inference for entropy, divergence, and related functionals of one or two probability distributions. Asymptotic properties of particular nonparametric estimators of such functionals are investigated. We consider estimation from both independent and dependent observations. The thesis consists of an introductory survey of the subject and some related theory and four papers (A-D). In Paper A, we consider a general class of entropy-type functionals which includes, for example, integer order Rényi entropy and certain Bregman divergences. We propose U-statistic estimators of these functionals based on the coincident or epsilon-close vector observations in the corresponding independent and identically distributed samples. We prove some asymptotic properties of the estimators such as consistency and asymptotic normality. Applications of the obtained results related to entropy maximizing distributions, stochastic databases, and image matching are discussed. In Paper B, we provide some important generalizations of the results for continuous distributions in Paper A. The consistency of the estimators is obtained under weaker density assumptions. Moreover, we introduce a class of functionals of quadratic order, including both entropy and divergence, and prove normal limit results for the corresponding estimators which are valid even for densities of low smoothness. The asymptotic properties of a divergence-based two-sample test are also derived. In Paper C, we consider estimation of the quadratic Rényi entropy and some related functionals for the marginal distribution of a stationary m-dependent sequence. We investigate asymptotic properties of the U-statistic estimators for these functionals introduced in Papers A and B when they are based on a sample from such a sequence. We prove consistency, asymptotic normality, and Poisson convergence under mild assumptions for the stationary m-dependent sequence. Applications of the results to time-series databases and entropy-based testing for dependent samples are discussed. In Paper D, we further develop the approach for estimation of quadratic functionals with m-dependent observations introduced in Paper C. We consider quadratic functionals for one or two distributions. The consistency and rate of convergence of the corresponding U-statistic estimators are obtained under weak conditions on the stationary m-dependent sequences. Additionally, we propose estimators based on incomplete U-statistics and show their consistency properties under more general assumptions.
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Padayachee, Jayanethie. "The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/16143.

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Bibliography: pages 128-129.
The elimination of some blurring property, such as the detector response function, from spectra has received a considerable amount of attention. The problem is usually complicated by the presence of noise in the data, and in general, there exists an infinite set of possible solutions which are consistent with the data within the bounds imposed by the noise. Such a problem is known, generally, as an ill-defined inverse problem. Many techniques have been developed in an attempt to solve inverse problems, for example the problem of deconvolution, but these techniques employ ad hoc modifications to solve different problems. Bayesian Statistics has been proved to be the only consistent method for solving inverse problems of the type where the information is expressed in terms of probability distributions. This dissertation presents results of applying the Bayesian formalism, together with the concepts of maximum information entropy and multiresolution pixons, to various inverse problems in ion beam analysis; The results of this method of deconvoluting Rutherford Backscattering Spectrometry (RBS) and Proton Induced X-ray Emission (PIXE) spectra are compared to the results from other deconvolution techniques, namely Fourier Transforms, Jansson's method and maximum entropy (MaxEnt) without pixons. All the deconvolution techniques show an improvement in the resolution of the RBS spectra but only the MaxEnt techniques show a significant improvement in the resolution of the PIXE spectra. The MaxEnt methods also produce physically acceptable results. The MaxEnt formalism was applied to the extraction of depth profiles from RBS and PIXE spectra and yielded good results. The technique was also used to deconvolute the beam profile from one-dimensional nuclear microprobe scans.
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Bright, Trevor James. "Non-fourier heat equations in solids analyzed from phonon statistics." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29710.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Zhang, Zhuomin; Committee Member: Kumar, Satish; Committee Member: Peterson, G. P. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Snyder, Selena Tyr. "Time Series Modeling of Clinical Electroencephalogram Data - An Information Theory Approach." Ohio University Honors Tutorial College / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1524830090342372.

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Books on the topic "Entropy (statistics)"

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Wegkamp, M. H. Entropy methods in statistical estimation. Amsterdam, The Netherlands: CWI, 1998.

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Jessop, Alan. Informed assessments: An introduction to information, entropy, and statistics. New York: Ellis Horwood, 1995.

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Jessop, A. Informed assessments: An introduction to information, entropy and statistics. New York: Ellis Horwood, 1995.

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Príncipe, J. C. Information theoretic learning: Renyi's entropy and kernel perspectives. New York: Springer, 2010.

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Jaynes, E. T. E.T. Jaynes: Papers on probability, statistics and statistical physics. Dordrecht: Kluwer, 1989.

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J, Erickson Gary, Rychert Joshua T, and Smith C. Ray 1933-, eds. Maximum entropy and Bayesian methods: Boise, Idaho, U.S.A., 1997 : proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis. Dordrecht: Kluwer Academic Publishers, 1998.

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International, Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis (17th 1997 Boise Idaho). Maximum entropy and Bayesian methods: Boise, Idaho, USA, 1997 : proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis. Boston: Kluwer Academic Publishers, 1998.

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Smith, C. Ray. Maximum-Entropy and Bayesian Methods in Inverse Problems. Dordrecht: Springer Netherlands, 1985.

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1941-, Justice James H., ed. Maximum entropy and Bayesian methods in applied statistics: Proceedings of the Fourth Maximum Entropy Workshop, University of Calgary, 1984. Cambridge [Cambridgeshire]: Cambridge University Press, 1986.

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Stummer, Wolfgang. Exponentials, diffusions, finance, entropy and information. Aachen: Shaker Verlag, 2004.

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Book chapters on the topic "Entropy (statistics)"

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Starzak, Michael E. "Applied Boltzmann Statistics." In Energy and Entropy, 165–79. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-77823-5_11.

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Volkenstein, Mikhail V. "Statistics and mechanics." In Entropy and Information, 85–111. Basel: Birkhäuser Basel, 2009. http://dx.doi.org/10.1007/978-3-0346-0078-1_5.

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Koks, Don. "Introductory Quantum Statistics." In Microstates, Entropy and Quanta, 385–444. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02429-1_7.

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Koks, Don. "Fermion Statistics in Metals." In Microstates, Entropy and Quanta, 445–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02429-1_8.

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Eshima, Nobuoki. "Entropy and Basic Statistics." In Behaviormetrics: Quantitative Approaches to Human Behavior, 1–27. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2552-0_1.

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Akaike, Hirotugu. "Prediction and Entropy." In A Celebration of Statistics, 1–24. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4613-8560-8_1.

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Akaike, Hirotugu. "Prediction and Entropy." In Springer Series in Statistics, 387–410. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-1694-0_30.

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Behara, M. "Polynomial Entropy." In Contributions to Econometrics and Statistics Today, 46–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70189-4_6.

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Koks, Don. "Boson Statistics in Blackbody Radiation." In Microstates, Entropy and Quanta, 481–534. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02429-1_9.

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Otáhal, Antonín. "Finiteness and Continuity of Differential Entropy." In Asymptotic Statistics, 415–19. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57984-4_36.

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Conference papers on the topic "Entropy (statistics)"

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Hennebert, Christine, Hicham Hossayni, and Cedric Lauradoux. "The entropy of wireless statistics." In 2014 European Conference on Networks and Communications (EuCNC). IEEE, 2014. http://dx.doi.org/10.1109/eucnc.2014.6882689.

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Azman, Muhammad Az-zuhri, Roslinazairimah Zakaria, Siti Zanariah Satari, and Noor Fadhilah Ahmad Radi. "Entropy of stable seasonal rainfall distribution in Kelantan." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982832.

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Ting, Chuan-Wei, and Ching-Yao Wang. "Online multiscale entropy estimation using distribution statistics." In 2012 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2012. http://dx.doi.org/10.1109/icspcc.2012.6335707.

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Chen, Yu-Jung, Noel Richard, Hilda Deborah, Aurelie Tournie, Anne Michelin, and Christine Andraud. "Statistics of hyperspectral data/image analysis: Entropy." In 2018 Colour and Visual Computing Symposium (CVCS). IEEE, 2018. http://dx.doi.org/10.1109/cvcs.2018.8496573.

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Matsuzoe, Hiroshi. "Information geometry of Bayesian statistics." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING (MAXENT 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4905989.

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Palukha, Uladzimir, and Yuriy Kharin. "Performance Analysis for Statistical Testing of Random and Pseudorandom Generators by Entropy Statistics." In 2019 International Conference on Information and Digital Technologies (IDT). IEEE, 2019. http://dx.doi.org/10.1109/dt.2019.8813395.

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Zare, Hossein, Mostafa Kaveh, and Arkady B. Khodursky. "Entropy-based analysis of ChIP-Sequencing data." In 2009 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2009. http://dx.doi.org/10.1109/gensips.2009.5174339.

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Maurin, Michel, Ali Mohammad-Djafari, Jean-François Bercher, and Pierre Bessiére. "Logarithm ubiquity in statistics, information, acoustics, [ellipsis (horizontal)]." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2011. http://dx.doi.org/10.1063/1.3573651.

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Chandrasekaran, Venkat, Jason K. Johnson, and Alan S. Willsky. "Maximum Entropy Relaxation for Graphical Model Selection Given Inconsistent Statistics." In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/ssp.2007.4301334.

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Ma, Zhiguo, Wenqing Li, and Jida Peng. "Application of Entropy Weight Theory in Evaluation of Rainstorm Risk." In 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/ammsa-17.2017.64.

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Reports on the topic "Entropy (statistics)"

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Larimore, Wallace E. Development of Statistical Methods Using Predictive Inference and Entropy. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada185459.

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Torres, Jesus V., Alfredo Cruz, and Jeff Duffany. How Do Statistical Detection Methods Compare to Entropy Measures. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada585591.

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Noyes, H. COMMENT ON 'STATISTICAL MECHANICAL ORIGIN OF THE ENTROPY OF A ROTATING, CHARGED BLACK HOLE'. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/1449646.

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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.
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Vargas-Herrera, Hernando, Juan Jose Ospina-Tejeiro, Carlos Alfonso Huertas-Campos, Adolfo León Cobo-Serna, Edgar Caicedo-García, Juan Pablo Cote-Barón, Nicolás Martínez-Cortés, et al. Monetary Policy Report - April de 2021. Banco de la República de Colombia, July 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2021.

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1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.
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