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Journal articles on the topic 'Maximum entropy'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Klimešová, D., and E. Ocelíková. "Spatial data modelling and maximum entropy theory." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (February 20, 2012): 80–83. http://dx.doi.org/10.17221/5080-agricecon.

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Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution function firstly and some distribution models, such as Gaussian distribution, may not suit to complicated distribution in practice. The paper shows the possibility of the approach based on the maximum entropy theory that can optimally describe the spatial data distribution and gives  the actual error estimation. 

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2

Harremoës, Peter, and Flemming Topsøe. "Maximum Entropy Fundamentals." Entropy 3, no. 3 (September 30, 2001): 191–226. http://dx.doi.org/10.3390/e3030191.

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3

Livesey, A. K., and J. Skilling. "Maximum entropy theory." Acta Crystallographica Section A Foundations of Crystallography 41, no. 2 (March 1, 1985): 113–22. http://dx.doi.org/10.1107/s0108767385000241.

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4

Politis, D. N. "Nonparametric maximum entropy." IEEE Transactions on Information Theory 39, no. 4 (July 1993): 1409–13. http://dx.doi.org/10.1109/18.243458.

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5

Shewry, M. C., and H. P. Wynn. "Maximum entropy sampling." Journal of Applied Statistics 14, no. 2 (January 1987): 165–70. http://dx.doi.org/10.1080/02664768700000020.

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6

Abbas, Ali E. "Maximum Entropy Utility." Operations Research 54, no. 2 (April 2006): 277–90. http://dx.doi.org/10.1287/opre.1040.0204.

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7

Rodriguez, Carlos C., and John Van Ryzin. "Maximum entropy histograms." Statistics & Probability Letters 3, no. 3 (June 1985): 117–20. http://dx.doi.org/10.1016/0167-7152(85)90047-1.

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8

Gull, S. F., and T. J. Newton. "Maximum entropy tomography." Applied Optics 25, no. 1 (January 1, 1986): 156. http://dx.doi.org/10.1364/ao.25.000156.

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9

Scharfenaker, Ellis, and Jangho Yang. "Maximum entropy economics." European Physical Journal Special Topics 229, no. 9 (July 2020): 1577–90. http://dx.doi.org/10.1140/epjst/e2020-000029-4.

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10

Jiang, Rui, Hui Zhou, Han Wang, and Shuzhi Sam Ge. "Maximum entropy searching." CAAI Transactions on Intelligence Technology 4, no. 1 (February 20, 2019): 1–8. http://dx.doi.org/10.1049/trit.2018.1058.

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11

Virgo, Nathaniel. "From Maximum Entropy to Maximum Entropy Production: A New Approach." Entropy 12, no. 1 (January 18, 2010): 107–26. http://dx.doi.org/10.3390/e12010107.

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12

Jana, P., S. K. Mazumder, and N. C. Das. "Optimal taxation policy maximum-entropy approach." Yugoslav Journal of Operations Research 13, no. 1 (2003): 95–105. http://dx.doi.org/10.2298/yjor0301095j.

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The object of this paper is firstly to present entropic measure of income inequality and secondly to develop maximum entropy approaches for the optimal reduction of income inequality through taxation. .

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13

Liu, Cheng-shi. "Nonsymmetric entropy and maximum nonsymmetric entropy principle." Chaos, Solitons & Fractals 40, no. 5 (June 15, 2009): 2469–74. http://dx.doi.org/10.1016/j.chaos.2007.10.039.

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14

Vakarin, E. V., and J. P. Badiali. "Entropy minimization within the maximum entropy approach." Journal of Physics: Conference Series 604 (April 17, 2015): 012017. http://dx.doi.org/10.1088/1742-6596/604/1/012017.

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15

Klika, Václav, Michal Pavelka, Petr Vágner, and Miroslav Grmela. "Dynamic Maximum Entropy Reduction." Entropy 21, no. 7 (July 22, 2019): 715. http://dx.doi.org/10.3390/e21070715.

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Any physical system can be regarded on different levels of description varying by how detailed the description is. We propose a method called Dynamic MaxEnt (DynMaxEnt) that provides a passage from the more detailed evolution equations to equations for the less detailed state variables. The method is based on explicit recognition of the state and conjugate variables, which can relax towards the respective quasi-equilibria in different ways. Detailed state variables are reduced using the usual principle of maximum entropy (MaxEnt), whereas relaxation of conjugate variables guarantees that the reduced equations are closed. Moreover, an infinite chain of consecutive DynMaxEnt approximations can be constructed. The method is demonstrated on a particle with friction, complex fluids (equipped with conformation and Reynolds stress tensors), hyperbolic heat conduction and magnetohydrodynamics.

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16

Le, Long V., Tae Jung Kim, Young Dong Kim, and David E. Aspnes. "Decoding ‘Maximum Entropy’ Deconvolution." Entropy 24, no. 9 (September 2, 2022): 1238. http://dx.doi.org/10.3390/e24091238.

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For over five decades, the mathematical procedure termed “maximum entropy” (M-E) has been used to deconvolve structure in spectra, optical and otherwise, although quantitative measures of performance remain unknown. Here, we examine this procedure analytically for the lowest two orders for a Lorentzian feature, obtaining expressions for the amount of sharpening and identifying how spurious structures appear. Illustrative examples are provided. These results enhance the utility of this widely used deconvolution approach to spectral analysis.

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17

Whiting, P. "Maximum Entropy Reflection Tomography." Exploration Geophysics 22, no. 2 (June 1991): 447–50. http://dx.doi.org/10.1071/eg991447.

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18

Lee, Jon. "Constrained Maximum-Entropy Sampling." Operations Research 46, no. 5 (October 1998): 655–64. http://dx.doi.org/10.1287/opre.46.5.655.

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19

Zhuang, X., R. M. Haralick, and Y. Zhao. "Maximum entropy image reconstruction." IEEE Transactions on Signal Processing 39, no. 6 (June 1991): 1478–80. http://dx.doi.org/10.1109/78.136565.

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20

Bunte, Christoph, and Amos Lapidoth. "Maximum Rényi Entropy Rate." IEEE Transactions on Information Theory 62, no. 3 (March 2016): 1193–205. http://dx.doi.org/10.1109/tit.2016.2521364.

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21

Herzog, Ulrike, and János A. Bergou. "Nonclassical maximum-entropy states." Physical Review A 56, no. 2 (August 1, 1997): 1658–61. http://dx.doi.org/10.1103/physreva.56.1658.

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22

Asadi, Majid, Nader Ebrahimi, G. G. Hamedani, and Ehsan S. Soofi. "Maximum dynamic entropy models." Journal of Applied Probability 41, no. 2 (June 2004): 379–90. http://dx.doi.org/10.1239/jap/1082999073.

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A formal approach to produce a model for the data-generating distribution based on partial knowledge is the well-known maximum entropy method. In this approach, partial knowledge about the data-generating distribution is formulated in terms of some information constraints and the model is obtained by maximizing the Shannon entropy under these constraints. Frequently, in reliability analysis the problem of interest is the lifetime beyond an age t. In such cases, the distribution of interest for computing uncertainty and information is the residual distribution. The information functions involving a residual life distribution depend on t, and hence are dynamic. The maximum dynamic entropy (MDE) model is the distribution with the density that maximizes the dynamic entropy for all t. We provide a result that relates the orderings of dynamic entropy and the hazard function for distributions with monotone densities. Applications include dynamic entropy ordering within some parametric families of distributions, orderings of distributions of lifetimes of systems and their components connected in series and parallel, record values, and formulation of constraints for the MDE model in terms of the evolution paths of the hazard function and mean residual lifetime function. In particular, we identify classes of distributions in which some well-known distributions, including the mixture of two exponential distributions and the mixture of two Pareto distributions, are the MDE models.

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23

Petchey, Owen L. "Maximum entropy in ecology." Oikos 119, no. 4 (April 12, 2010): 577. http://dx.doi.org/10.1111/j.1600-0706.2009.18503.x.

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24

Anstreicher, Kurt M., Marcia Fampa, Jon Lee, and Joy Williams. "Maximum-entropy remote sampling." Discrete Applied Mathematics 108, no. 3 (March 2001): 211–26. http://dx.doi.org/10.1016/s0166-218x(00)00217-1.

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25

Asadi, Majid, Nader Ebrahimi, G. G. Hamedani, and Ehsan S. Soofi. "Maximum dynamic entropy models." Journal of Applied Probability 41, no. 02 (June 2004): 379–90. http://dx.doi.org/10.1017/s0021900200014376.

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A formal approach to produce a model for the data-generating distribution based on partial knowledge is the well-known maximum entropy method. In this approach, partial knowledge about the data-generating distribution is formulated in terms of some information constraints and the model is obtained by maximizing the Shannon entropy under these constraints. Frequently, in reliability analysis the problem of interest is the lifetime beyond an age t. In such cases, the distribution of interest for computing uncertainty and information is the residual distribution. The information functions involving a residual life distribution depend on t, and hence are dynamic. The maximum dynamic entropy (MDE) model is the distribution with the density that maximizes the dynamic entropy for all t. We provide a result that relates the orderings of dynamic entropy and the hazard function for distributions with monotone densities. Applications include dynamic entropy ordering within some parametric families of distributions, orderings of distributions of lifetimes of systems and their components connected in series and parallel, record values, and formulation of constraints for the MDE model in terms of the evolution paths of the hazard function and mean residual lifetime function. In particular, we identify classes of distributions in which some well-known distributions, including the mixture of two exponential distributions and the mixture of two Pareto distributions, are the MDE models.

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26

Dimandja, Jean-Marie D., Mihkel Kaljurand, John B. Phillips, and José Valentı́n. "Maximum entropy chromatogram reconstruction." Analytica Chimica Acta 371, no. 1 (September 1998): 1–8. http://dx.doi.org/10.1016/s0003-2670(98)00271-2.

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27

Holm, Juhani. "Maximum entropy Lorenz curves." Journal of Econometrics 59, no. 3 (October 1993): 377–89. http://dx.doi.org/10.1016/0304-4076(93)90031-y.

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28

Minardi, E., and G. Lampis. "Maximum entropy Tokamak configurations." Plasma Physics and Controlled Fusion 32, no. 10 (October 1, 1990): 819–31. http://dx.doi.org/10.1088/0741-3335/32/10/005.

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29

Karloff, Howard, and Kenneth E. Shirley. "Maximum Entropy Summary Trees." Computer Graphics Forum 32, no. 3pt1 (June 2013): 71–80. http://dx.doi.org/10.1111/cgf.12094.

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30

Verkley, W. T. M., and T. Gerkema. "On Maximum Entropy Profiles." Journal of the Atmospheric Sciences 61, no. 8 (April 2004): 931–36. http://dx.doi.org/10.1175/1520-0469(2004)061<0931:omep>2.0.co;2.

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31

von der Linden, W. "Maximum-entropy data analysis." Applied Physics A: Materials Science & Processing 60, no. 2 (January 1, 1995): 155–65. http://dx.doi.org/10.1007/s003390050086.

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32

Kang, Bingyi, and Yong Deng. "The Maximum Deng Entropy." IEEE Access 7 (2019): 120758–65. http://dx.doi.org/10.1109/access.2019.2937679.

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33

Schroeder, J. "The Maximum Entropy Method." Zeitschrift für Physikalische Chemie 208, Part_1_2 (January 1999): 288–89. http://dx.doi.org/10.1524/zpch.1999.208.part_1_2.288.

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34

BRYAN, R. K. "MAXIMUM ENTROPY DATA ANALYSIS." Le Journal de Physique Colloques 47, no. C5 (August 1986): C5–43—C5–53. http://dx.doi.org/10.1051/jphyscol:1986506.

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35

Lee, Jon, and Joy Lind. "Generalized maximum-entropy sampling." INFOR: Information Systems and Operational Research 58, no. 2 (February 21, 2019): 168–81. http://dx.doi.org/10.1080/03155986.2018.1533774.

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36

Choi, B. S. "Multivariate Maximum Entropy Spectrum." Journal of Multivariate Analysis 46, no. 1 (July 1993): 56–60. http://dx.doi.org/10.1006/jmva.1993.1046.

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37

Grandy, W. T. "Maximum entropy in action." Journal of Atmospheric and Terrestrial Physics 57, no. 1 (January 1995): 99. http://dx.doi.org/10.1016/0021-9169(95)90013-6.

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38

von der Linden, W. "Maximum-entropy data analysis." Applied Physics A 60, no. 2 (February 1995): 155–65. http://dx.doi.org/10.1007/bf01538241.

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39

Piantadosi, Julia, Phil Howlett, and Jonathan Borwein. "Copulas with maximum entropy." Optimization Letters 6, no. 1 (October 28, 2010): 99–125. http://dx.doi.org/10.1007/s11590-010-0254-2.

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40

Dreyer, Wolfgang, and Matthias Kunik. "Maximum entropy principle revisited." Continuum Mechanics and Thermodynamics 10, no. 6 (December 1, 1998): 331–47. http://dx.doi.org/10.1007/s001610050097.

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41

Ramos, F. M., H. F. Campos Velho, J. C. Carvalho, and N. J. Ferreira. "Beyond maximum entropy: novel approaches to entropic regularization." Computer Physics Communications 121-122 (September 1999): 722. http://dx.doi.org/10.1016/s0010-4655(06)70125-4.

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42

Dobovišek, Andrej, Rene Markovič, Milan Brumen, and Aleš Fajmut. "The maximum entropy production and maximum Shannon information entropy in enzyme kinetics." Physica A: Statistical Mechanics and its Applications 496 (April 2018): 220–32. http://dx.doi.org/10.1016/j.physa.2017.12.111.

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43

Knockaert, L. "Maximum a posteriori maximum entropy order determination." IEEE Transactions on Signal Processing 45, no. 6 (June 1997): 1553–59. http://dx.doi.org/10.1109/78.599997.

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44

VAKARIN, E. V., and J. P. BADIALI. "A LINK BETWEEN THE MAXIMUM ENTROPY APPROACH AND THE VARIATIONAL ENTROPY FORM." Modern Physics Letters B 25, no. 22 (August 30, 2011): 1821–28. http://dx.doi.org/10.1142/s0217984911027054.

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The maximum entropy approach operating with quite general entropy measure and constraint is considered. It is demonstrated that for a conditional or parametrized probability distribution f(x|μ), there is a "universal" relation among the entropy rate and the functions appearing in the constraint. This relation allows one to translate the specificities of the observed behavior θ(μ) into the amount of information on the relevant random variable x at different values of the parameter μ. It is shown that the recently proposed variational formulation of the entropic functional can be obtained as a consequence of this relation, that is from the maximum entropy principle. This resolves certain puzzling points that appeared in the variational approach.

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45

XIAO, CHANGMING, and LIXIN HUANG. "ENTROPIC FORCE IN A CLOSED IDEAL GAS." Modern Physics Letters B 20, no. 09 (April 10, 2006): 495–500. http://dx.doi.org/10.1142/s0217984906010731.

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For a closed thermodynamic system of ideal gas, the entropic force is studied in this paper. The results show that the entropic force arises when the entropy is deviated from its equilibrium maximum value by an external force. This entropic force resists the entropy deviation enlarging, and will drive the entropy back to its maximum value if the external forces disappear.

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46

Eguchi, Shinto, Osamu Komori, and Shogo Kato. "Projective Power Entropy and Maximum Tsallis Entropy Distributions." Entropy 13, no. 10 (September 26, 2011): 1746–64. http://dx.doi.org/10.3390/e13101746.

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47

Al-Nasser, Amjad D. "The Journey from Entropy to Generalized Maximum Entropy." Journal of Quantitative Methods 3, no. 1 (February 2019): 1–7. http://dx.doi.org/10.29145/2019/jqm/030101.

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48

Landau, H. J. "Maximum entropy and maximum likelihood in spectral estimation." IEEE Transactions on Information Theory 44, no. 3 (May 1998): 1332–36. http://dx.doi.org/10.1109/18.669428.

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49

Roversi, P., E. Blanc, R. Morris, C. Flensburg, and G. Bricogne. "Maximum likelihood density modification under maximum entropy control." Acta Crystallographica Section A Foundations of Crystallography 58, s1 (August 6, 2002): c251. http://dx.doi.org/10.1107/s010876730209503x.

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50

Beretta, Gian Paolo. "Modeling Non-Equilibrium Dynamics of a Discrete Probability Distribution: General Rate Equation for Maximal Entropy Generation in a Maximum-Entropy Landscape with Time-Dependent Constraints." Entropy 10, no. 3 (September 2008): 160–82. http://dx.doi.org/10.3390/entropy-e10030160.

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