Relevant bibliographies by topics / Entropy maximization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Entropy maximization'

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Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Entropy maximization"

1

Athreya, K. B. "Entropy maximization." Proceedings - Mathematical Sciences 119, no. 4 (September 2009): 531–39. http://dx.doi.org/10.1007/s12044-009-0049-5.

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Censor, Yair, and Joseph Segman. "On Block-Iterative Entropy Maximization." Journal of Information and Optimization Sciences 8, no. 3 (September 1987): 275–91. http://dx.doi.org/10.1080/02522667.1987.10698894.

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Martı́nez, S., F. Nicolás, F. Pennini, and A. Plastino. "Tsallis’ entropy maximization procedure revisited." Physica A: Statistical Mechanics and its Applications 286, no. 3-4 (November 2000): 489–502. http://dx.doi.org/10.1016/s0378-4371(00)00359-9.

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Janečka, Adam, and Michal Pavelka. "Gradient Dynamics and Entropy Production Maximization." Journal of Non-Equilibrium Thermodynamics 43, no. 1 (January 26, 2018): 1–19. http://dx.doi.org/10.1515/jnet-2017-0005.

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AbstractWe compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs–-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell–Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.
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Ratnayake, L. L. "Intercity auto trip estimation for Sri Lanka using entropy maximization." Canadian Journal of Civil Engineering 16, no. 2 (April 1, 1989): 200–201. http://dx.doi.org/10.1139/l89-036.

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Recently, techniques such as calibration of demand models and entropy maximization have been used to estimate origin–destination (O–D) matrices. To the author's knowledge, there has been no reported work on an O–D estimation for a nationwide network. This paper describes a methodology to estimate intercity auto traffic for Sri Lanka using entropy maximization. Some of these results are then compared with the values obtained from a known demand model and the actual data. Key words: O–D matrices, entropy maximization, Sri Lanka, link volume, intercity.
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Ré, Christopher, and D. Suciu. "Understanding cardinality estimation using entropy maximization." ACM Transactions on Database Systems 37, no. 1 (February 2012): 1–31. http://dx.doi.org/10.1145/2109196.2109202.

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Miller, Gad, and David Horn. "Probability Density Estimation Using Entropy Maximization." Neural Computation 10, no. 7 (October 1, 1998): 1925–38. http://dx.doi.org/10.1162/089976698300017205.

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We propose a method for estimating probability density functions and conditional density functions by training on data produced by such distributions. The algorithm employs new stochastic variables that amount to coding of the input, using a principle of entropy maximization. It is shown to be closely related to the maximum likelihood approach. The encoding step of the algorithm provides an estimate of the probability distribution. The decoding step serves as a generative mode, producing an ensemble of data with the desired distribution. The algorithm is readily implemented by neural networks, using stochastic gradient ascent to achieve entropy maximization.
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Haegeman, Bart, and Michel Loreau. "Limitations of entropy maximization in ecology." Oikos 117, no. 11 (October 28, 2008): 1700–1710. http://dx.doi.org/10.1111/j.1600-0706.2008.16539.x.

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Rouge, Richard, and Nicole El Karoui. "Pricing Via Utility Maximization and Entropy." Mathematical Finance 10, no. 2 (April 2000): 259–76. http://dx.doi.org/10.1111/1467-9965.00093.

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Zou, Jieping, and Greg Holloway. "Entropy maximization tendency in topographic turbulence." Journal of Fluid Mechanics 263 (March 25, 1994): 361–74. http://dx.doi.org/10.1017/s0022112094004155.

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Numerical simulations of geostrophic turbulence above topography are used to compare (a) nonlinear generation of system entropy, S, (b) selective damping of enstrophy and (c) development of vorticity–topography correlation. In the damped cases, S initially increases, approaching a quasi-equilibrium (maximum S subject to the instantaneous, though decaying, energy and enstrophy). When strongly scale-selective damping is applied, onset of the vorticity–topography correlation follows the timescales for enstrophy decay. During the period of decay, it is shown that nonlinear interaction continues to generate S, offsetting in part the loss of S to explicit damping.
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Dissertations / Theses on the topic "Entropy maximization"

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Bæcklund, Anna. "Maximization of the Wehrl Entropy in Finite Dimensions : Maximization of the Wehrl Entropy in Finite Dimensions." Thesis, KTH, Fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120594.

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Glocer, Karen A. "Entropy regularization and soft margin maximization /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2009. http://uclibs.org/PID/11984.

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Garvey, Jennie Hill. "Independent component analysis by entropy maximization (infomax)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FGarvey.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Frank E. Kragh. "June 2007." Includes bibliographical references (p. 103). Also available in print.
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4

Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." University of Sydney. School of Electrical and Information Engineering, 2005. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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5

Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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6

Khabou, Mohamed Ali. "Improving shared weight neural networks generalization using regularization theory and entropy maximization /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9953870.

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7

Fabro, Adriano Todorovic. "Análise estocástica do comportamento dinâmico de estruturas via métodos probabilísticos." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265418.

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Orientador: José Roberto de França Arruda
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
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Resumo: Esta dissertação tem como objetivo geral levar 'a realidade industrial subsídios para a modelagem e análise de sistemas mecânicos lineares com variabilidade, assim como metodologias computacionais para quantificação de incertezas, para fins de aplicação em projeto. Neste sentido, foram realizados estudos sobre técnicas de modelagem e análise estocástica de sistemas mecânicos lineares aplicadas, inicialmente, a algumas estruturas simples, de baixo custo computacional, por meio de simulações em MatLabR. Propõe-se uma abordagem probabilística para a modelagem de incertezas baseada no Princípio da Máxima Entropia para a flexibilidade relativa a uma trinca aberta e não propagante em uma barra modelada através do Método do Elemento Espectral (SEM). Também é apresentada uma abordagem para o tratamento de problemas de campo aleatório utilizando o SEM, onde são utilizadas soluções analíticas da decomposição de Karhunen-Lo'eve. Uma formulação para elementos de viga do tipo Euler-Bernoulli é apresentada e um exemplo em que a rigidez à flexão é modelada como um campo aleatório Gaussiano é tratado. Uma abordagem para análise estocástica do comportamento dinâmico de uma tampa de compressor hermético é proposta. Uma aproximação por elementos finitos obtida com o software Ansys R foi utilizada para representar o comportamento determinístico de uma tampa de compressor, e duas abordagens de modelagem estocástica são comparadas. Um ensaio experimental foi realizado com tampas nominalmente idênticas, sendo medidas apenas frequências naturais com excitação por impacto, de modo a se poder compará-las com os valores obtidos teoricamente
Abstract: This dissertation has as a general objective to bring to the industrial reality subsidies for modeling and analysis of linear mechanical systems with variability, as well as computational methodologies to the uncertainty quantification, aiming industrial design applications. In that sense, theoretical studies about stochastic modeling and analysis for mechanical linear systems were performed. They were applied, firstly, to simple and computationally low cost structures using MatlabR. In that sense, a probabilistic modeling approach based on the Maximum Entropy Principle was proposed to treat the flexibility related to an open and nonpropagating crack in a rod modeled using the Spectral Element Method (SEM). An approach for the treatment of random field problems using SEM, which uses analytical solutions of the Karhunen-Lo'eve Decomposition, is also addressed. An Euler-Bernoulli beam formulation was used, and an example where the flexural stiffness is modeled as a Gaussian random field is presented. A finite element approximation obtained with the software Ansys R was used to represent the deterministic dynamic behavior of a compressor cap shell, and two stochastic modeling approaches were compared. Experiments were performed using nominally identical cap samples. Natural frequencies were measured using impact excitation in order to compare with the theoretical results
Mestrado
Mecanica dos Sólidos e Projeto Mecanico
Mestre em Engenharia Mecânica
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8

Medeiros, Richerland Pinto [UNESP]. "Inferência de emoções em fragmentos de textos obtidos do Facebook." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150974.

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Esta pesquisa tem como objetivo analisar o uso da técnica estatística de aprendizado de máquina Maximização de Entropia, voltado para tarefas de processamento de linguagem natural na inferência de emoções em textos obtidos da rede social Facebook. Foram estudados os conceitos primordiais das tarefas de processamento de linguagem natural, os conceitos inerentes a teoria da informação, bem como o aprofundamento no conceito de um modelo entrópico como classificador de textos. Os dados utilizados na presente pesquisa foram obtidos de textos curtos, ou seja, textos com no máximo 500 caracteres. A técnica em questão foi abordada dentro do aprendizado supervisionado de máquina, logo, parte dos dados coletados foram usados como exemplos marcados dentro de um conjunto de classes predefinidas, a fim de induzir o mecanismo de aprendizado a selecionar a classe de emoção mais provável dado o exemplo analisado. O método proposto obteve índice de assertividade médio de 90%, baseado no modelo de validação cruzada.
This research aims to analyze the use of entropy maximization machine learning statistical technique, focused on natural language processing tasks in the inferencing of emotions in short texts from Facebook social network. Were studied the primary concepts of natural language processing tasks, IT intrinsic concepts, as well as deepening the concept of Entropy model as a text classifier. All data used for this research came from short texts found in social networks and had 500 characters or less. The model was used within supervised machine learning, therefore, part of the collected data was used as examples marked within a set of predefined classes in order to induce the learning mechanism to select the most probable emotion class given the analyzed sample. The method has obtained the mean accuracy rate of 90%, based on the cross-validation model.
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Hatefi, Armin. "Mixture model analysis with rank-based samples." Statistica Sinica, 2013. http://hdl.handle.net/1993/23849.

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Simple random sampling (SRS) is the most commonly used sampling design in data collection. In many applications (e.g., in fisheries and medical research) quantification of the variable of interest is either time-consuming or expensive but ranking a number of sampling units, without actual measurement on them, can be done relatively easy and at low cost. In these situations, one may use rank-based sampling (RBS) designs to obtain more representative samples from the underlying population and improve the efficiency of the statistical inference. In this thesis, we study the theory and application of the finite mixture models (FMMs) under RBS designs. In Chapter 2, we study the problems of Maximum Likelihood (ML) estimation and classification in a general class of FMMs under different ranked set sampling (RSS) designs. In Chapter 3, deriving Fisher information (FI) content of different RSS data structures including complete and incomplete RSS data, we show that the FI contained in each variation of the RSS data about different features of FMMs is larger than the FI contained in their SRS counterparts. There are situations where it is difficult to rank all the sampling units in a set with high confidence. Forcing rankers to assign unique ranks to the units (as RSS) can lead to substantial ranking error and consequently to poor statistical inference. We hence focus on the partially rank-ordered set (PROS) sampling design, which is aimed at reducing the ranking error and the burden on rankers by allowing them to declare ties (partially ordered subsets) among the sampling units. Studying the information and uncertainty structures of the PROS data in a general class of distributions, in Chapter 4, we show the superiority of the PROS design in data analysis over RSS and SRS schemes. In Chapter 5, we also investigate the ML estimation and classification problems of FMMs under the PROS design. Finally, we apply our results to estimate the age structure of a short-lived fish species based on the length frequency data, using SRS, RSS and PROS designs.
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Ludovic, Moreau. "A Contribution in Stochastic Control Applied to Finance and Insurance." Phd thesis, Université Paris Dauphine - Paris IX, 2012. http://tel.archives-ouvertes.fr/tel-00737624.

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Le but de cette thèse est d'apporter une contribution à la problématique de valorisation de produits dérivés en marchés incomplets. Nous considérons tout d'abord les cibles stochastiques introduites par Soner et Touzi (2002) afin de traiter le problème de sur-réplication, et récemment étendues afin de traiter des approches plus générales par Bouchard, Elie et Touzi (2009). Nous généralisons le travail de Bouchard {\sl et al} à un cadre plus général où les diffusions sont sujettes à des sauts. Nous devons considérer dans ce cas des contrôles qui prennent la forme de fonctions non bornées, ce qui impacte de façon non triviale la dérivation des EDP correspondantes. Notre deuxième contribution consiste à établir une version des cibles stochastiques qui soit robuste à l'incertitude de modèle. Dans un cadre abstrait, nous établissons une version faible du principe de programmation dynamique géométrique de Soner et Touzi (2002), et nous dérivons, dans un cas d'EDS controllées, l'équation aux dérivées partielles correspondantes, au sens des viscosités. Nous nous intéressons ensuite à un exemple de couverture partielle sous incertitude de Knightian. Finalement, nous nous concentrons sur le problème de valorisation de produits dérivées {\sl hybrides} (produits dérivés combinant finance de marché et assurance). Nous cherchons plus particulièrement à établir une condition suffisante sous laquelle une règle de valorisation (populaire dans l'industrie), consistant à combiner l'approches actuarielle de mutualisation avec une approche d'arbitrage, soit valable.
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Books on the topic "Entropy maximization"

1

Golan, Amos. Entropy Maximization. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0004.

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In this chapter I develop the essential maximum entropy procedure, which is an inversion procedure for inferring an unknown probability distribution function from incomplete information. The formulation here is the root of info-metrics and is grounded in the motivations provided in Chapter 2 and the metrics defined in Chapter 3. Once the basic maximum entropy problem is defined and the solution is derived via the method of Lagrange multipliers, I derive and discuss its dual formulation. I then define and discuss the concept of conjugate variables, which is related to the Lagrange multipliers. Throughout, the mathematical derivations are supported by graphical illustrations and supplemented with heuristic arguments and with numerous examples in ideal settings.
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2

Gell-Mann, Murray, and Constantino Tsallis, eds. Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.001.0001.

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A great variety of complex phenomena in many scientific fields exhibit power-law behavior, reflecting a hierarchical or fractal structure. Many of these phenomena seem to be susceptible to description using approaches drawn from thermodynamics or statistical mechanics, particularly approaches involving the maximization of entropy and of Boltzmann-Gibbs statistical mechanics and standard laws in a natural way. The book addresses the interdisciplinary applications of these ideas, and also on various phenomena that could possibly be quantitatively describable in terms of these ideas.
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Rickard, David. Framboids. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190080112.001.0001.

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Framboids may be the most astonishing and abundant natural features you have never heard of. These microscopic spherules of golden pyrite consist of thousands of even smaller microcrystals, often arranged in stunning geometric arrays. There are probably 1030 on Earth, and they are forming at a rate of 1020 every second. This means that there are a billion times more framboids than sand grains on Earth, and a million times more framboids than stars in the observable universe. They are all around us: they can be found in rocks of all ages and in present-day sediments, soils, and natural waters. The sulfur in the pyrite is mainly produced by bacteria, and many framboids contain organic matter. They are formed through burst nucleation of supersaturated solutions of iron and sulfide, followed by limited crystal growth in diffusion-dominated stagnant sediments. The framboids self-assemble as surface free energy is minimized and the microcrystals are attracted to each other by surface forces. Self-organization occurs through entropy maximization, and the microcrystals rotate into their final positions through Brownian motion. The final shape of the framboids is often actually polygonal or partially facetted rather than spherical, as icosahedral microcrystal packing develops. Their average diameter is around 6 microns and the average microcrystal size is about 0.1 microns. There is no significant change in these dimensions with time: the framboid is an exceptionally stable structure, and the oldest may be 2.9 billion years old. This means that they provide samples of the chemistry of ancient environments.
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Book chapters on the topic "Entropy maximization"

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Zabarankin, Michael, and Stan Uryasev. "Entropy Maximization." In Statistical Decision Problems, 53–70. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8471-4_5.

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Borwein, Jonathan M., and Qiji J. Zhu. "Entropy Maximization in Finance." In Springer Proceedings in Mathematics & Statistics, 275–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36568-4_18.

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Fröhner, F. H. "Entropy Maximization in Nuclear Physics." In Maximum Entropy and Bayesian Methods, 93–107. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3460-6_9.

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Shipley, Bill. "Entropy Maximization and Species Abundance." In Encyclopedia of Complexity and Systems Science, 2903–18. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_174.

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Ayres, Robert. "A Brief History of Ideas: Energy, Entropy and Evolution." In Energy, Complexity and Wealth Maximization, 15–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30545-5_3.

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Kaushik, Raghav, Christopher Ré, and Dan Suciu. "General Database Statistics Using Entropy Maximization." In Database Programming Languages, 84–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03793-1_6.

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Uniyal, Nitin, Girish Dobhal, and Alok Darshan Kothiyal. "An Improvement in Key Domain Maximization Technique by Entropy Maximization." In Algorithms for Intelligent Systems, 681–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6307-6_70.

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Shapiro, Alexander A., and Erling H. Stenby. "Principle of Entropy Maximization for Nonequilibrium Steady States." In Thermal Nonequilibrium Phenomena in Fluid Mixtures, 61–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45791-7_4.

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Censor, Yair, Tommy Elfving, and Gabor T. Herman. "Special-Purpose Algorithms for Linearly Constrained Entropy Maximization." In Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems, 241–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3961-5_14.

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Pletscher, Patrick, Cheng Soon Ong, and Joachim M. Buhmann. "Entropy and Margin Maximization for Structured Output Learning." In Machine Learning and Knowledge Discovery in Databases, 83–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8_6.

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Conference papers on the topic "Entropy maximization"

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Fradkov, Alexander L., Dmitry S. Shalymov, and Anton V. Proskurnikov. "Speed-gradient entropy maximization in networks." In 2016 IEEE Conference on Norbert Wiener in the 21st Century (21CW). IEEE, 2016. http://dx.doi.org/10.1109/norbert.2016.7547461.

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Xu, Dahai. "Compact formulation of Network Entropy Maximization." In 2012 46th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2012. http://dx.doi.org/10.1109/ciss.2012.6310762.

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Ré, Christopher, and Dan Suciu. "Understanding cardinality estimation using entropy maximization." In the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807085.1807095.

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Dhavlle, Abhijitt, Raj Mehta, Setareh Rafatirad, Houman Homayoun, and Sai Manoj Pudukotai Dinakarrao. "Entropy-Shield:Side-Channel Entropy Maximization for Timing-based Side-Channel Attacks." In 2020 21st International Symposium on Quality Electronic Design (ISQED). IEEE, 2020. http://dx.doi.org/10.1109/isqed48828.2020.9137008.

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Graham, Rishi, and Jorge Cortes. "Cooperative adaptive sampling via approximate entropy maximization." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC 2009). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400511.

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Jin, Qinggui, Guirong Wang, and Yuancheng Liu. "Blind Signal Separation by Entropy Maximization (INFOMAX)." In 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5600111.

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Heidemann, Dirk. "A Note on the Entropy Maximization Principle." In Second International Conference on Transportation and Traffic Studies (ICTTS ). Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40503(277)43.

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Mailloux, Guy E., Hail Mallouche, Rita Noumeir, and Raymond Lemieux. "MART algorithm for SPECT and entropy maximization." In OE/LASE'93: Optics, Electro-Optics, & Laser Applications in Science& Engineering, edited by Randall L. Barbour and Mark J. Carvlin. SPIE, 1993. http://dx.doi.org/10.1117/12.151193.

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Savas, Yagiz, Melkior Ornik, Murat Cubuktepe, and Ufuk Topcu. "Entropy Maximization for Constrained Markov Decision Processes." In 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2018. http://dx.doi.org/10.1109/allerton.2018.8636066.

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Fu, Geng-Shen, Zois Boukouvalas, and Tulay Adali. "Density estimation by entropy maximization with kernels." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178300.

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