Relevant bibliographies by topics / Maximum entropy

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Academic literature on the topic 'Maximum entropy'

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Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Maximum entropy"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Maximum entropy"

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Sognnæs, Ida Andrea Braathen. "Maximum Entropy and Maximum Entropy Production in Macroecology." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for fysikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12651.

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The Maximum Entropy Theory of Ecology (METE), developed by John Harte, presents an entirely new method of making inferences in ecology. The method is based on the established mathematical procedure of Maximum Information Entropy (MaxEnt), developed by Edwin T. Jaynes, and is used to derive a range of important relationships in macroecology. The Maximum Entropy Production (MEP) principle is a more recent theory. This principle was used by Paltridge to successfully predict the climate on Earth in 1975. It has been suggested that this principle can be used for predicting the evolution of ecosystems over time in the framework of METE. This idea is at the very frontier of Harte's theory. This thesis investigates the hypothesis that the information entropy defined in METE is described by the MEP principle.I show that the application of the MEP principle to the information entropy in METE leads to a range of conceptual and mathematical difficulties. I show that the initial hypothesis alone cannot predict the time rate of change, but that it does predict that the number of individual organisms and the total metabolic rate of an ecosystem will continue to grow indefinitely, whereas the number of species will approach one.I also conduct a thorough review of the MEP literature and discuss the possibility of an application of the MEP principle to METE based on analogies. I also study a proof of the MEP principle published by Dewar in 2003 and 2005 in order to investigate the possibility of an application based on first principles. I conclude that the MEP principle has a low probability of success if applied directly to the information entropy in METE.One of the most central relationships derived in METE is the expected number of species in a plot of area $A$. I conduct a numerical simulation in order to study the variance of the actual number of species in a collection of plots. I then suggest two methods to be used for comparison between predictions and observations in METE.I also conduct a numerical study of selectied stability properties of Paltridge's climate model and conclude that none of these can explain the observed MEP state in nature.
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Charter, Mark Keith. "Maximum entropy pharmacokinetics." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316691.

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Patterson, Brett Alexander. "Maximum entropy data analysis." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240969.

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Xie, Yong. "Maximum entropy in crystallography." Thesis, De Montfort University, 2003. http://hdl.handle.net/2086/4220.

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Purahoo, K. "Maximum entropy data analysis." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260038.

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Robinson, David Richard Terence. "Developments in maximum entropy data analysis." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307063.

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McLean, Andrew Lister. "Applications of maximum entropy data analysis." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319161.

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Sears, Timothy Dean, and tim sears@biogreenoil com. "Generalized Maximum Entropy, Convexity and Machine Learning." The Australian National University. Research School of Information Sciences and Engineering, 2008. http://thesis.anu.edu.au./public/adt-ANU20090525.210315.

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This thesis identifies and extends techniques that can be linked to the principle of maximum entropy (maxent) and applied to parameter estimation in machine learning and statistics. Entropy functions based on deformed logarithms are used to construct Bregman divergences, and together these represent a generalization of relative entropy. The framework is analyzed using convex analysis to charac- terize generalized forms of exponential family distributions. Various connections to the existing machine learning literature are discussed and the techniques are applied to the problem of non-negative matrix factorization (NMF).
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Oliveira, V. A. "Maximum entropy image restoration in nuclear medicine." Thesis, University of Southampton, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235282.

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Касьянов, Володимир, and Андрій Гончаренко. "SUBJECTIVE ENTROPY MAXIMUM PRINCIPLE AND ITS APPLICATIONS." Thesis, Національний авіаційний університет, 2017. https://er.nau.edu.ua/handle/NAU/48996.

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Books on the topic "Maximum entropy"

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Squartini, Tiziano, and Diego Garlaschelli. Maximum-Entropy Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69438-2.

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Fampa, Marcia, and Jon Lee. Maximum-Entropy Sampling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6.

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Wu, Nailong. The Maximum Entropy Method. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60629-8.

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The maximum entropy method. Berlin: Springer, 1997.

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Wu, Nailong. The Maximum Entropy Method. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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Xie, Yong. Maximum entropy in crystallography. Leicester: De Montfort University, 2004.

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Karmeshu, ed. Entropy Measures, Maximum Entropy Principle and Emerging Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36212-8.

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missing], [name. Entropy measures, maximum entropy principle, and emerging applications. Berlin: Springer Verlag, 2004.

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The method of maximum entropy. Singapore: World Scientific, 1995.

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Bevensee, R. M. Maximum entropy solutions to scientific problems. Englewood Cliffs, N.J: Prentice Hall, 1993.

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Book chapters on the topic "Maximum entropy"

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Fampa, Marcia, and Jon Lee. "Upper bounds." In Maximum-Entropy Sampling, 31–143. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6_3.

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Fampa, Marcia, and Jon Lee. "Environmental monitoring." In Maximum-Entropy Sampling, 145–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6_4.

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Fampa, Marcia, and Jon Lee. "The problem and basic properties." In Maximum-Entropy Sampling, 1–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6_1.

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Fampa, Marcia, and Jon Lee. "Branch-and-bound." In Maximum-Entropy Sampling, 17–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6_2.

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Fampa, Marcia, and Jon Lee. "Opportunities." In Maximum-Entropy Sampling, 157–62. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13078-6_5.

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Bajkova, A. T. "Generalized Maximum Entropy. Comparison with Classical Maximum Entropy." In Maximum Entropy and Bayesian Methods, 407–14. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-2217-9_50.

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Hoch, Jeffrey C. "Maximum Entropy Reconstruction." In Encyclopedia of Biophysics, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-35943-9_337-1.

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Shekhar, Shashi, and Hui Xiong. "Bayesian Maximum Entropy." In Encyclopedia of GIS, 39. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_94.

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Marti, Kurt. "Maximum Entropy Techniques." In Stochastic Optimization Methods, 323–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0_8.

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Hoch, Jeffrey C. "Maximum Entropy Reconstruction." In Encyclopedia of Biophysics, 1431–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-16712-6_337.

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Conference papers on the topic "Maximum entropy"

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Sukumar, N. "Maximum Entropy Approximation." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2005. http://dx.doi.org/10.1063/1.2149812.

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Cheeseman, Peter. "Generalized Maximum Entropy." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2005. http://dx.doi.org/10.1063/1.2149816.

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Seghouane, Abd-Krim, Luc Knockaert, Kevin H. Knuth, Ariel Caticha, Julian L. Center, Adom Giffin, and Carlos C. Rodríguez. "Maximum a Posteriori Maximum Entropy Signal Denoising." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, 2007. http://dx.doi.org/10.1063/1.2821270.

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Xiang, Gang, and Vladik Kreinovich. "Extending maximum entropy techniques to entropy constraints." In NAFIPS 2010 - 2010 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2010. http://dx.doi.org/10.1109/nafips.2010.5548264.

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Harremoës, Peter. "Maximum entropy and sufficiency." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4985352.

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Aras, Efe, and Thomas A. Courtade. "Sharp Maximum-Entropy Comparisons." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517779.

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Cicalese, Ferdinando, and Ugo Vaccaro. "Maximum Entropy Interval Aggregations." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437780.

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Grendar, M. "Empirical Maximum Entropy Methods." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423302.

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Heiric, D., and D. Zazula. "Modulus maximum image energy using maximum entropy." In 47th International Symposium ELMAR, 2005. IEEE, 2005. http://dx.doi.org/10.1109/elmar.2005.193640.

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Grendar, M. "Maximum Probability and Maximum Entropy methods: Bayesian interpretation." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1751390.

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Reports on the topic "Maximum entropy"

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Mastin, G. A., and R. J. Hanson. Maximum entropy signal restoration with linear programming. Office of Scientific and Technical Information (OSTI), May 1988. http://dx.doi.org/10.2172/7043116.

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Fellman, Laura. The Genetic Algorithm and Maximum Entropy Dice. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7120.

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Silver, R. N., H. Roeder, A. F. Voter, and J. D. Kress. Chebyshev recursion methods: Kernel polynomials and maximum entropy. Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/119974.

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Chen, Stanley F., and Ronald Rosenfeld. A Gaussian Prior for Smoothing Maximum Entropy Models. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada360974.

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Lozar, Robert, Scott Tweddale, Charles Ehlschlaeger, Carey Baxter, and Jeffrey Burkhalter. Testing maximum entropy analysis to define population distributions. Engineer Research and Development Center (U.S.), September 2018. http://dx.doi.org/10.21079/11681/29352.

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Silver, R. N., H. Roeder, A. F. Voter, and J. D. Kress. Chebyshev moment problems: Maximum entropy and kernel polynomial methods. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/195578.

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Woodbury, A., Y. Jiang, and S. Painter. Bayesian and maximum entropy inversion of highly heterogeneous aquifers. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/299514.

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Archambault, M., R. W. MacCormack, and C. F. Edwards. A Maximum Entropy Moment Closure Approach to Describing Spray Flows. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada397975.

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Lapedes, A. S., B. G. Giraud, L. C. Liu, and G. D. Stormo. A Maximum Entropy Formalism for Disentangling Chains of Correlated Sequence Positions. Office of Scientific and Technical Information (OSTI), August 1998. http://dx.doi.org/10.2172/763147.

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Hogden, J. An articulatorily constrained, maximum entropy approach to speech recognition and speech coding. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/432946.

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