Literatura académica sobre el tema "Probability-based method"
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Artículos de revistas sobre el tema "Probability-based method"
Tang, Li, Jie-zhong Zou y Wen-sheng Yang. "A numerical method based on probability theory". Journal of Central South University of Technology 10, n.º 2 (junio de 2003): 159–61. http://dx.doi.org/10.1007/s11771-003-0060-4.
Texto completoGrigoriu, Mircea y Katerina D. Papoulia. "Effective conductivity by a probability-based local method". Journal of Applied Physics 98, n.º 3 (agosto de 2005): 033706. http://dx.doi.org/10.1063/1.1993775.
Texto completoKruger, D. y W. T. Penzhorn. "Adaptive probability estimation based on IIR filtering method". Electronics Letters 38, n.º 25 (2002): 1659. http://dx.doi.org/10.1049/el:20021111.
Texto completoLi, Zhi-Gang, Jun-Gang Zhou y Bo-Ying Liu. "System Reliability Analysis Method Based on Fuzzy Probability". International Journal of Fuzzy Systems 19, n.º 6 (8 de agosto de 2017): 1759–67. http://dx.doi.org/10.1007/s40815-017-0363-5.
Texto completoHuang, Yingping, Ross McMurran, Gunwant Dhadyalla y R. Peter Jones. "Probability based vehicle fault diagnosis: Bayesian network method". Journal of Intelligent Manufacturing 19, n.º 3 (19 de enero de 2008): 301–11. http://dx.doi.org/10.1007/s10845-008-0083-7.
Texto completoHe, Liangli, Zhenzhou Lu y Kaixuan Feng. "A novel estimation method for failure-probability-based-sensitivity by conditional probability theorem". Structural and Multidisciplinary Optimization 61, n.º 4 (21 de diciembre de 2019): 1589–602. http://dx.doi.org/10.1007/s00158-019-02437-x.
Texto completoZheqi, Zhu, Ren Bo, Zhang Xiaofeng, Zeng Hang, Xue Tao y Chen Qingge. "Neural network-based probability forecasting method of aviation safety". IOP Conference Series: Materials Science and Engineering 1043, n.º 3 (1 de enero de 2021): 032063. http://dx.doi.org/10.1088/1757-899x/1043/3/032063.
Texto completoMauriello, Paolo y Domenico Patella. "A DATA-ADAPTIVE PROBABILITY-BASED FAST ERT INVERSION METHOD". Progress In Electromagnetics Research 97 (2009): 275–90. http://dx.doi.org/10.2528/pier09092307.
Texto completoZhao, Yongxiang. "PROBABILITY-BASED CYCLIC STRESS-STRAIN CURVES AND ESTIMATION METHOD". Chinese Journal of Mechanical Engineering 36, n.º 08 (2000): 102. http://dx.doi.org/10.3901/jme.2000.08.102.
Texto completoYang, Xing, Xiaodong Hu y Zhiqing Li. "The conditional risk probability-based seawall height design method". International Journal of Naval Architecture and Ocean Engineering 7, n.º 6 (noviembre de 2015): 1007–19. http://dx.doi.org/10.1515/ijnaoe-2015-0070.
Texto completoTesis sobre el tema "Probability-based method"
PINHO, Luis Gustavo Bastos. "Building new probability distributions: the composition method and a computer based method". Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24966.
Texto completoMade available in DSpace on 2018-07-03T21:14:00Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Luis Gustavo Bastos Pinho.pdf: 3785410 bytes, checksum: 4a1cf7340340bd8ff994a74abb62ba0e (MD5) Previous issue date: 2017-01-17
FACEPE
We discuss the creation of new probability distributions for continuous data in two distinct approaches. The first one is, to our knowledge, novelty and consists of using Estimation of Distribution Algorithms (EDAs) to obtain new cumulative distribution functions. This class of algorithms work as follows. A population of solutions for a given problem is randomly selected from a space of candidates, which may contain candidates that are not feasible solutions to the problem. The selection occurs by following a set of probability rules that, initially, assign a uniform distribution to the space of candidates. Each individual is ranked by a fitness criterion. A fraction of the most fit individuals is selected and the probability rules are then adjusted to increase the likelihood of obtaining solutions similar to the most fit in the current population. The algorithm iterates until the set of probability rules are able to provide good solutions to the problem. In our proposal, the algorithm is used to generate cumulative distribution functions to model a given continuous data set. We tried to keep the mathematical expressions of the new functions as simple as possible. The results were satisfactory. We compared the models provided by the algorithm to the ones in already published papers. In every situation, the models proposed by the algorithms had advantages over the ones already published. The main advantage is the relative simplicity of the mathematical expressions obtained. Still in the context of computational tools and algorithms, we show the performance of simple neural networks as a method for parameter estimation in probability distributions. The motivation for this was the need to solve a large number of non linear equations when dealing with SAR images (SAR stands for synthetic aperture radar) in the statistical treatment of such images. The estimation process requires solving, iteratively, a non-linear equation. This is repeated for every pixel and an image usually consists of a large number of pixels. We trained a neural network to approximate an estimator for the parameter of interest. Once trained, the network can be fed the data and it will return an estimate of the parameter of interest without the need of iterative methods. The training of the network can take place even before collecting the data from the radar. The method was tested on simulated and real data sets with satisfactory results. The same method can be applied to different distributions. The second part of this thesis shows two new probability distribution classes obtained from the composition of already existing ones. In each situation, we present the new class and general results such as power series expansions for the probability density functions, expressions for the moments, entropy and alike. The first class is obtained from the composition of the beta-G and Lehmann-type II classes. The second class, from the transmuted-G and Marshall-Olkin-G classes. Distributions in these classes are compared to already existing ones as a way to illustrate the performance of applications to real data sets.
Discutimos a criação de novas distribuições de probabilidade para dados contínuos em duas abordagens distintas. A primeira é, ao nosso conhecimento, inédita e consiste em utilizar algoritmos de estimação de distribuição para a obtenção de novas funções de distribuição acumulada. Algoritmos de estimação de distribuição funcionam da seguinte forma. Uma população de soluções para um determinado problema é extraída aleatoriamente de um conjunto que denominamos espaço de candidatos, o qual pode possuir candidatos que não são soluções viáveis para o problema. A extração ocorre de acordo com um conjunto de regras de probabilidade, as quais inicialmente atribuem uma distribuição uniforme ao espaço de candidatos. Cada indivíduo na população é classificado de acordo com um critério de desempenho. Uma porção dos indivíduos com melhor desempenho é escolhida e o conjunto de regras é adaptado para aumentar a probabilidade de obter soluções similares aos melhores indivíduos da população atual. O processo é repetido por um número de gerações até que a distribuição de probabilidade das soluções sorteadas forneça soluções boas o suficiente. Em nossa aplicação, o problema consiste em obter uma função de distribuição acumulada para um conjunto de dados contínuos qualquer. Tentamos, durante o processo, manter as expressões matemáticas das distribuições geradas as mais simples possíveis. Os resultados foram satisfatórios. Comparamos os modelos providos pelo algoritmo a modelos publicados em outros artigos. Em todas as situações, os modelos obtidos pelo algoritmo apresentaram vantagens sobre os modelos dos artigos publicados. A principal vantagem é a expressão matemática reduzida. Ainda no contexto do uso de ferramentas computacionais e algoritmos, mostramos como utilizar redes neurais simples para a estimação de parâmetros em distribuições de probabilidade. A motivação para tal aplicação foi a necessidade de resolver iterativamente um grande número de equações não lineares no tratamento estatístico de imagens obtidas de SARs (synthetic aperture radar). O processo de estimação requer a solução de uma equação por métodos iterativos e isso é repetido para cada pixel na imagem. Cada imagem possui um grande número de pixels, em geral. Pensando nisso, treinamos uma rede neural para aproximar o estimador para esse parâmetro. Uma vez treinada, a rede é alimentada com as janelas referente a cada pixel e retorna uma estimativa do parâmetro, sem a necessidade de métodos iterativos. O treino ocorre antes mesmo da obtenção dos dados do radar. O método foi testado em conjuntos de dados reais e fictícios com ótimos resultados. O mesmo método pode ser aplicado a outras distribuições. A segunda parte da tese exibe duas classes de distribuições de probabilidade obtidas a partir da composição de classes existentes. Em cada caso, apresentamos a nova classe e resultados gerais tais como expansões em série de potência para a função densidade de probabilidade, expressões para momentos, entropias e similares. A primeira classe é a composição das classes beta-G e Lehmann-tipo II. A segunda classe é obtida a partir das classes transmuted-G e Marshall-Olkin-G. Distribuições pertencentes a essas classes são comparadas a outras já existentes como maneira de ilustrar o desempenho em aplicações a dados reais.
Hoang, Tam Minh Thi 1960. "A joint probability model for rainfall-based design flood estimation". Monash University, Dept. of Civil Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8892.
Texto completoAlkhairy, Ibrahim H. "Designing and Encoding Scenario-based Expert Elicitation for Large Conditional Probability Tables". Thesis, Griffith University, 2020. http://hdl.handle.net/10072/390794.
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Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Mansour, Rami. "Reliability Assessment and Probabilistic Optimization in Structural Design". Doctoral thesis, KTH, Hållfasthetslära (Avd.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183572.
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Chapman, Gary. "Computer-based musical composition using a probabilistic algorithmic method". Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341603.
Texto completoLiu, Xiang. "Identification of indoor airborne contaminant sources with probability-based inverse modeling methods". Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3337124.
Texto completoDedovic, Ines Verfasser], Jens-Rainer [Akademischer Betreuer] Ohm y Dorit [Akademischer Betreuer] [Merhof. "Efficient probability distribution function estimation for energy based image segmentation methods / Ines Dedovic ; Jens-Rainer Ohm, Dorit Merhof". Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1130871541/34.
Texto completoDedović, Ines [Verfasser], Jens-Rainer Akademischer Betreuer] Ohm y Dorit [Akademischer Betreuer] [Merhof. "Efficient probability distribution function estimation for energy based image segmentation methods / Ines Dedovic ; Jens-Rainer Ohm, Dorit Merhof". Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1130871541/34.
Texto completoKraum, Martin. "Fischer-Tropsch synthesis on supported cobalt based Catalysts Influence of various preparation methods and supports on catalyst activity and chain growth probability /". [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=959085181.
Texto completoGood, Norman Markus. "Methods for estimating the component biomass of a single tree and a stand of trees using variable probability sampling techniques". Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37097/1/37097_Good_2001.pdf.
Texto completoLibros sobre el tema "Probability-based method"
Center, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.
Buscar texto completoCenter, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.
Buscar texto completoStructural performance: Probability-based assessement. London: ISTE, 2011.
Buscar texto completo1966-, Bogaert Patrick y Serre Marc L. 1967-, eds. Temporal GIS: Advanced functions for field-based applications. Berlin: Springer, 2001.
Buscar texto completoMarkov chain Monte Carlo simulations and their statistical analysis: With web-based Fortran code. Hackensack, NJ: World Scientific, 2004.
Buscar texto completoauthor, Thompson Simon G., ed. Mendelian randomization: Methods for using genetic variants in causal estimation. Boca Raton: CRC Press, Taylor & Francis Group, 2015.
Buscar texto completoStatistical methods in psychiatry research and SPSS. Toronto: Apple Academic Press, 2015.
Buscar texto completoNichols, Eve K. Expanding access to investigational therapies for HIV infection and AIDS: March 12-13, 1990, conference summary. Washington, D.C: National Academy Press, 1991.
Buscar texto completoBeyond second opinions: Making choices about fertility treatment. Berkeley: University of California Press, 1998.
Buscar texto completoEUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.
Buscar texto completoCapítulos de libros sobre el tema "Probability-based method"
Song, Xu, Guoqiang Li, Ying Li y Yanning Zhang. "A Probability-Based Object Tracking Method". En Lecture Notes in Computer Science, 595–602. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42057-3_75.
Texto completoKim, Phil Young, Ji Won Kim y Yunsick Sung. "Bayesian Probability-Based Hand Property Control Method". En Lecture Notes in Electrical Engineering, 251–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-17314-6_33.
Texto completoDing, Yuxin, Longfei Wang, Rui Wu y Fuxing Xue. "Source Detection Method Based on Propagation Probability". En Lecture Notes in Computer Science, 179–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94307-7_15.
Texto completoZhang, Di, Huifang Ma, Junjie Jia y Li Yu. "A Tag Probability Correlation Based Microblog Recommendation Method". En Neural Information Processing, 491–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_55.
Texto completoZhao, Chengdong, Xuhui Wang y Jie Shao. "Method of Image Fusion Based on Improved Probability Theory". En Advances in Intelligent and Soft Computing, 241–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30223-7_39.
Texto completoLiu, Ying y Yuefeng Zheng. "A Network Attack Recognition Method Based on Probability Target Graph". En Emerging Trends in Intelligent and Interactive Systems and Applications, 778–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63784-2_96.
Texto completoSon, Junhyuck y Yunsick Sung. "Bayesian Probability and User Experience-Based Smart UI Design Method". En Lecture Notes in Electrical Engineering, 245–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-17314-6_32.
Texto completoKim, Phil Young, Yunsick Sung y Jonghyuk Park. "Bayesian Probability-Based Motion Estimation Method in Ubiquitous Computing Environments". En Advances in Computer Science and Ubiquitous Computing, 593–98. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_84.
Texto completoYang, Li, Di He, Peilin Liu y Wenxian Yu. "Fingerprint Positioning Method of Satellite Signal Based on Probability Distribution". En China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume II, 211–20. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0937-2_18.
Texto completoXie, Lixia, Siyu Liu, Hongyu Yang y Liang Zhang. "A Defect Level Assessment Method Based on Weighted Probability Ensemble". En Cyberspace Safety and Security, 293–300. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18067-5_21.
Texto completoActas de conferencias sobre el tema "Probability-based method"
Li Zhi-gang, Zhou Jun-gang y Liu Bo-ying. "System reliability analysis method based fuzzy probability". En 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy). IEEE, 2016. http://dx.doi.org/10.1109/ifuzzy.2016.8004956.
Texto completoYang, Yonghui, Fei Deng, Yunqiang Yan y Feng Gao. "A Fault Localization Method Based on Conditional Probability". En 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2019. http://dx.doi.org/10.1109/qrs-c.2019.00050.
Texto completoXiong, Min y Zhangjun Liu. "Fuzzy Probability Method-Based Assessment of Green Energy". En 2011 Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2011. http://dx.doi.org/10.1109/appeec.2011.5748833.
Texto completoWang, Ji-He, Jin-Xiu Zhang y Xi-Bin Cao. "Probability based collision monitoring method within formation flying". En 2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics (ISSCAA). IEEE, 2008. http://dx.doi.org/10.1109/isscaa.2008.4776292.
Texto completoLee, Jaeyeon y Wooram Park. "A probability-based path planning method using fuzzy logic". En 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942973.
Texto completoChao, Wang, Qiu Jing, Liu Guan-jun, Zhang Yong y Zhao Chen-xu. "Testability verification based on sequential probability ratio test method". En 2013 IEEE AUTOTESTCON. IEEE, 2013. http://dx.doi.org/10.1109/autest.2013.6645066.
Texto completoEftekhari-Moghadam, Amir-Masud y Marjan Abdechiri. "An unsupervised evaluation method based on probability density function". En 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/isie.2010.5636328.
Texto completoBi, Qian, Shuang Wu, Yong Huang, Yalong Zhu y Zhuofei Hu. "A Target Location Method Based on Swarm Probability Fusion". En 2021 IEEE 4th International Conference on Electronics Technology (ICET). IEEE, 2021. http://dx.doi.org/10.1109/icet51757.2021.9451063.
Texto completoLu, Jiang, Wen Wu, Zhenyong Zhang y Jinyuan Zhang. "Probability Calculation of Equipment Impact Based on Reliability Method". En 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33147.
Texto completoLiu, Zhangjun, Min Xiong y Hongrui Ding. "Fuzzy Probability Method-Based Risk Assessment of Engineering Project". En 2010 International Conference on Internet Technology and Applications (iTAP). IEEE, 2010. http://dx.doi.org/10.1109/itapp.2010.5566233.
Texto completoInformes sobre el tema "Probability-based method"
Wright, T. A simple method for probability proportional to size (. pi. ps) sampling without replacement based on ranks. Office of Scientific and Technical Information (OSTI), junio de 1987. http://dx.doi.org/10.2172/6504729.
Texto completoLister, C. J., H. M. King, E. A. Atkinson, L. E. Kung y R. Nairn. A probability-based method to generate qualitative petroleum potential maps: adapted for and illustrated using ArcGIS. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/311225.
Texto completoKott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, agosto de 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.
Texto completoYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko y Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], febrero de 2020. http://dx.doi.org/10.31812/123456789/3683.
Texto completoPilkevych, Ihor, Oleg Boychenko, Nadiia Lobanchykova, Tetiana Vakaliuk y Serhiy Semerikov. Method of Assessing the Influence of Personnel Competence on Institutional Information Security. CEUR Workshop Proceedings, abril de 2021. http://dx.doi.org/10.31812/123456789/4374.
Texto completoClausen, Jay, Vuong Truong, Sophia Bragdon, Susan Frankenstein, Anna Wagner, Rosa Affleck y Christopher Williams. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), septiembre de 2022. http://dx.doi.org/10.21079/11681/45625.
Texto completoTarko, Andrew P., Qiming Guo y Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.
Texto completoWeller, Joel I., Ignacy Misztal y Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, marzo de 2015. http://dx.doi.org/10.32747/2015.7594404.bard.
Texto completoAhmad, Noshin S., Raul Pineda-Mendez, Fahad Alqahtani, Mario Romero, Jose Thomaz y Andrew P. Tarko. Effective Design and Operation of Pedestrian Crossings. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317438.
Texto completoDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe y Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, diciembre de 2021. http://dx.doi.org/10.53328/uxuo4751.
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