Добірка наукової літератури з теми "Probabilities – Data processing"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Probabilities – Data processing".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Probabilities – Data processing"
Vaidogas, Egidijus Rytas. "Bayesian Processing of Data on Bursts of Pressure Vessels." Information Technology and Control 50, no. 4 (December 16, 2021): 607–26. http://dx.doi.org/10.5755/j01.itc.50.4.29690.
Повний текст джерелаIvanov, A. I., E. N. Kuprianov, and S. V. Tureev. "Neural network integration of classical statistical tests for processing small samples of biometrics data." Dependability 19, no. 2 (June 16, 2019): 22–27. http://dx.doi.org/10.21683/1729-2646-2019-19-2-22-27.
Повний текст джерелаRomansky, Radi. "Mathematical Model Investigation of a Technological Structure for Personal Data Protection." Axioms 12, no. 2 (January 18, 2023): 102. http://dx.doi.org/10.3390/axioms12020102.
Повний текст джерелаTkachenko, Kirill. "PROVIDING A DEPENDABLE OPERATION OF THE DATA PROCESSING SYSTEM WITH INTERVAL CHANGES IN THE FLOW CHARACTERISTICS BASED ON ANALYTICAL SIMULATIONS." Automation and modeling in design and management 2021, no. 3-4 (December 30, 2021): 25–30. http://dx.doi.org/10.30987/2658-6436-2021-3-4-25-30.
Повний текст джерелаGroot, Perry, Christian Gilissen, and Michael Egmont-Petersen. "Error probabilities for local extrema in gene expression data." Pattern Recognition Letters 28, no. 15 (November 2007): 2133–42. http://dx.doi.org/10.1016/j.patrec.2007.06.017.
Повний текст джерелаČajka, Radim, and Martin Krejsa. "Measured Data Processing in Civil Structure Using the DOProC Method." Advanced Materials Research 859 (December 2013): 114–21. http://dx.doi.org/10.4028/www.scientific.net/amr.859.114.
Повний текст джерелаChervyakov, N. I., P. A. Lyakhov, and A. R. Orazaev. "3D-generalization of impulse noise removal method for video data processing." Computer Optics 44, no. 1 (February 2020): 92–100. http://dx.doi.org/10.18287/2412-6179-co-577.
Повний текст джерелаLi, Qiude, Qingyu Xiong, Shengfen Ji, Junhao Wen, Min Gao, Yang Yu, and Rui Xu. "Using fine-tuned conditional probabilities for data transformation of nominal attributes." Pattern Recognition Letters 128 (December 2019): 107–14. http://dx.doi.org/10.1016/j.patrec.2019.08.024.
Повний текст джерелаJain, Kirti. "Sentiment Analysis on Twitter Airline Data." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3767–70. http://dx.doi.org/10.22214/ijraset.2021.35807.
Повний текст джерелаBuhmann, Joachim, and Hans Kühnel. "Complexity Optimized Data Clustering by Competitive Neural Networks." Neural Computation 5, no. 1 (January 1993): 75–88. http://dx.doi.org/10.1162/neco.1993.5.1.75.
Повний текст джерелаДисертації з теми "Probabilities – Data processing"
Sun, Liwen, and 孙理文. "Mining uncertain data with probabilistic guarantees." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45705392.
Повний текст джерелаNavas, Portella Víctor. "Statistical modelling of avalanche observables: criticality and universality." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670764.
Повний текст джерелаEls sistemes complexos es poden entendre com entitats compostes per un gran nombre d’elements en interacció on la seva resposta global i emergent no es pot derivar de les lleis particulars que caracteritzen cadascun dels seus constituents. Els observables que caracteritzen aquests sistemes es poden observar a diferents escales i, sovint, mostren propietats interessants tals com la manca d’escales característiques i autosimilitud. En aquest context, les funcions amb lleis de potència prenen un paper important en la descripció d’aquests observables. La presència de lleis de potència s’assimila a la situació dels fenòmens crítics en equilibri, on algunes quantitats termodinàmiques mostren un comportament funcional similar prop d’un punt crític. Diferents sistemes complexos es poden agrupar en la mateixa classe d’universalitat quan les funcions de lleis de potència que caracteritzen els seus observables tenen els mateixos exponents. Quan són conduïts externament, la resposta d’alguns sistemes complexos segueix el que s’anomonena un procès d’allaus: una resposta col·lectiva del sistema caracteritzada per seguir una dinàmica intermitent, amb sobtats increments d’activitat separats per períodes de silenci. Aquesta mena de sistemes fora de l’equilibri es poden trobar en diferents disciplines tals com la sismologia, astrofísica, ecologia, epidemologia o finances, per mencionar alguns. Les allaus estan caracteritzades per un conjunt d’observables tals com la mida, l’energia o la durada. Quan aquests observables mostren una manca d’escales característiques, les seves distribucions de probabilitat es poden modelitzar estadísticament per distribucions de lleis de potència. S’anomenen allaus crítiques aquelles en que els seus observables es poden caracteritzar per aquestes distribucions. En aquest sentit, els conceptes de criticalitat i universalitat, els quals estan ben definits per fenòmens en equilibri, es poden extendre per les distribucions de probabilitat que descriuen els observables de les allaus en sistemes fora de l’equilibri. L’objectiu principal d’aquesta tesi doctoral és proporcionar mètodes estadístics robusts per tal de caracteritzar la criticalitat i la universalitat en allaus corresponents a dades empíriques. Degut a les limitacions en l’adquisició de dades, les dades empíriques sovint cobreixen un rang petit d’observació, dificultant que es pugui establir un determinat comportament en forma de llei de potència de manera inequívoca. Amb l’objectiu de discutir els conceptes de criticalitat i universalitat en allaus, es consideraran dos sistemes diferents: els terratrèmols i els esdeveniments d’emissió acústica que es generen durant experiments de compressió de materials porosos al laboratori (labquakes). Les tècniques desenvolupades en aquesta tesi doctoral estan enfocades principalment a la distribució de la mida dels terratrèmols i labquakes, altrament coneguda com a llei de Gutenberg-Richter. No obstant, aquests mètodes són molt més generals i es poden aplicar a qualsevol observable de les allaus. Les tècniques estadístistiques proporcionades en aquest treball poden també ajudar al pronòstic de terratrèmols. Durant anys, la teoria d’esforços de Coulomb s’ha utilitzat en sismologia per tal d’entendre com els terratrèmols desencadenen l’ocurrència d’altres de nous. Els models de terratrèmols que relacionen la taxa d’ocurrència de rèpliques i l’esforç de Coulomb després d’un gran esdeveniment, assumeixen que la distribució de la mida dels terratrèmols no està afectada pel canvi en l’esforç de Coulomb. Diverses anàlisi estadístiques s’aplicaran per tal de comprovar si la distribució de magnituds és sensible al signe de l’esforç de Coulomb. S’ha provat que l’ús de tècniques estadístiques avançades en l’anàlisi de sistemes complexos és útil i necessari per tal d’aportar rigor als resultats empírics i, en particular, a problemes d’anàlisi de riscos.
Franco, Samuel. "Searching for long transient gravitational waves in the LIGO-Virgo data." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01062708.
Повний текст джерелаAntelo, Junior Ernesto Willams Molina. "Estimação conjunta de atraso de tempo subamostral e eco de referência para sinais de ultrassom." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2616.
Повний текст джерелаEm ensaios não destrutivos por ultrassom, o sinal obtido a partir de um sistema de aquisição de dados real podem estar contaminados por ruído e os ecos podem ter atrasos de tempo subamostrais. Em alguns casos, esses aspectos podem comprometer a informação obtida de um sinal por um sistema de aquisição. Para lidar com essas situações, podem ser utilizadas técnicas de estimativa de atraso temporal (Time Delay Estimation ou TDE) e também técnicas de reconstrução de sinais, para realizar aproximações e obter mais informações sobre o conjunto de dados. As técnicas de TDE podem ser utilizadas com diversas finalidades na defectoscopia, como por exemplo, para a localização precisa de defeitos em peças, no monitoramento da taxa de corrosão em peças, na medição da espessura de um determinado material e etc. Já os métodos de reconstrução de dados possuem uma vasta gama de aplicação, como nos NDT, no imageamento médico, em telecomunicações e etc. Em geral, a maioria das técnicas de estimativa de atraso temporal requerem um modelo de sinal com precisão elevada, caso contrário, a localização dessa estimativa pode ter sua qualidade reduzida. Neste trabalho, é proposto um esquema alternado que estima de forma conjunta, uma referência de eco e atrasos de tempo para vários ecos a partir de medições ruidosas. Além disso, reinterpretando as técnicas utilizadas a partir de uma perspectiva probabilística, estendem-se suas funcionalidades através de uma aplicação conjunta de um estimador de máxima verossimilhança (Maximum Likelihood Estimation ou MLE) e um estimador máximo a posteriori (MAP). Finalmente, através de simulações, resultados são apresentados para demonstrar a superioridade do método proposto em relação aos métodos convencionais.
Abstract (parágrafo único): In non-destructive testing (NDT) with ultrasound, the signal obtained from a real data acquisition system may be contaminated by noise and the echoes may have sub-sample time delays. In some cases, these aspects may compromise the information obtained from a signal by an acquisition system. To deal with these situations, Time Delay Estimation (TDE) techniques and signal reconstruction techniques can be used to perform approximations and also to obtain more information about the data set. TDE techniques can be used for a number of purposes in the defectoscopy, for example, for accurate location of defects in parts, monitoring the corrosion rate in pieces, measuring the thickness of a given material, and so on. Data reconstruction methods have a wide range of applications, such as NDT, medical imaging, telecommunications and so on. In general, most time delay estimation techniques require a high precision signal model, otherwise the location of this estimate may have reduced quality. In this work, an alternative scheme is proposed that jointly estimates an echo model and time delays for several echoes from noisy measurements. In addition, by reinterpreting the utilized techniques from a probabilistic perspective, its functionalities are extended through a joint application of a maximum likelihood estimator (MLE) and a maximum a posteriori (MAP) estimator. Finally, through simulations, results are presented to demonstrate the superiority of the proposed method over conventional methods.
Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.
Повний текст джерелаJiang, Bin Computer Science & Engineering Faculty of Engineering UNSW. "Probabilistic skylines on uncertain data." 2007. http://handle.unsw.edu.au/1959.4/40712.
Повний текст джерелаMurison, Robert. "Problems in density estimation for independent and dependent data." Phd thesis, 1993. http://hdl.handle.net/1885/136654.
Повний текст джерелаKanetsi, Khahiso. "Annual peak rainfall data augmentation - A Bayesian joint probability approach for catchments in Lesotho." Thesis, 2017. https://hdl.handle.net/10539/25567.
Повний текст джерелаThe main problem to be investigated is how short duration data records can be augmented using existing data from nearby catchments with data with long periods of record. The purpose of the investigation is to establish a method of improving hydrological data using data from a gauged catchment to improve data from an ungauged catchment. The investigation is undertaken using rainfall data for catchments in Lesotho. Marginal distributions describing the annual maximum rainfall for the catchments, and a joint distribution of pairs of catchments were established. The parameters of these distributions were estimated using the Bayesian – Markov Chain Monte Carlo approach, and using both the single-site (univariate) estimation and the two-site (bivariate) estimations. The results of the analyses show that for catchments with data with short periods of record, the precision of the estimated location and scale parameters improved when the estimates were carried out using the two-site (bivariate) method. Rainfall events predicted using bivariate analyses parameters were generally higher than the univariate analyses parameters. From the results, it can be concluded that the two-site approach can be used to improve the precision of the rainfall predictions for catchments with data with short periods of record. This method can be used in practice by hydrologists and design engineers to enhance available data for use in designs and assessments.
CK2018
Wang, Haiou. "Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling." Thesis, 2001. http://hdl.handle.net/1957/28769.
Повний текст джерелаGraduation date: 2002
Fazelnia, Ghazal. "Optimization for Probabilistic Machine Learning." Thesis, 2019. https://doi.org/10.7916/d8-jm7k-2k98.
Повний текст джерелаКниги з теми "Probabilities – Data processing"
Kelly, Brendan. Data management & probability module. Toronto, ON: Ontario Ministry of Education and Training, 1998.
Знайти повний текст джерелаBasic probability using MATLAB. Boston: PWS Pub. Co., 1995.
Знайти повний текст джерелаPetrushin, V. N. Informat︠s︡ionnai︠a︡ chuvstvitelʹnostʹ kompʹi︠u︡ternykh algoritmov. Moskva: FIZMATLIT, 2010.
Знайти повний текст джерелаAizaki, Hideo. Stated preference methods using R. Boca Raton: CRC Press, Taylor & Francis Group, 2015.
Знайти повний текст джерелаT, Callender J., ed. Exploring probability and statistics with spreadsheets. London: Prentice Hall, 1995.
Знайти повний текст джерелаProbability and random processes: Using MATLAB with applications to continuous and discrete time systems. Chicago: Irwin, 1997.
Знайти повний текст джерелаRozhkov, V. A. Metody i sredstva statisticheskoĭ obrabotki i analiza informat︠s︡ii ob obstanovke v mirovom okeane na primere gidrometeorologii. Obninsk: VNIIGMI-MT︠S︡D, 2009.
Знайти повний текст джерелаAndrews, D. F. Calculations with random variables using mathematica. Toronto: University of Toronto, Dept. of Statistics, 1990.
Знайти повний текст джерелаPetersen, E. R. PROPS+: Proabilistic and optimization spreadsheets plus what-if-solver. Reading, MA: Addison-Wesley, 1994.
Знайти повний текст джерелаRozhkov, V. A. Metody i sredstva statisticheskoĭ obrabotki i analiza informat︠s︡ii ob obstanovke v mirovom okeane na primere gidrometeorologii. Obninsk: VNIIGMI-MT︠S︡D, 2009.
Знайти повний текст джерелаЧастини книг з теми "Probabilities – Data processing"
Pegoraro, Marco, Bianka Bakullari, Merih Seran Uysal, and Wil M. P. van der Aalst. "Probability Estimation of Uncertain Process Trace Realizations." In Lecture Notes in Business Information Processing, 21–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_2.
Повний текст джерелаKosheleva, Olga, and Vladik Kreinovich. "Beyond p-Boxes and Interval-Valued Moments: Natural Next Approximations to General Imprecise Probabilities." In Statistical and Fuzzy Approaches to Data Processing, with Applications to Econometrics and Other Areas, 133–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45619-1_11.
Повний текст джерелаKukar, Matjaž, Igor Kononenko, and Ciril Grošelj. "Automated Diagnostics of Coronary Artery Disease." In Data Mining, 1043–63. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch053.
Повний текст джерелаKukar, Matjaž, Igor Kononenko, and Ciril Grošelj. "Automated Diagnostics of Coronary Artery Disease." In Medical Applications of Intelligent Data Analysis, 91–112. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1803-9.ch006.
Повний текст джерелаChiverton, John, and Kevin Wells. "PV Modeling of Medical Imaging Systems." In Benford's Law. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691147611.003.0018.
Повний текст джерелаHarff, J. E., and R. A. Olea. "From Multivariate Sampling To Thematic Maps With An Application To Marine Geochemistry." In Computers in Geology - 25 Years of Progress. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195085938.003.0027.
Повний текст джерелаТези доповідей конференцій з теми "Probabilities – Data processing"
Lokse, Sigurd, and Robert Jenssen. "Ranking Using Transition Probabilities Learned from Multi-Attribute Data." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462132.
Повний текст джерелаReznik, A. L., A. A. Soloviev, and A. V. Torgov. "On the statistics of anomalous clumps in random point images." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.11.90.030.
Повний текст джерелаKo, Hsiao-Han, Kuo-Jin Tseng, Li-Min Wei, and Meng-Hsiun Tsai. "Possible Disease-Link Genetic Pathways Constructed by Hierarchical Clustering and Conditional Probabilities of Ovarian Carcinoma Microarray Data." In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2010. http://dx.doi.org/10.1109/iihmsp.2010.8.
Повний текст джерелаZhang, Xiaodong, Ying Min Low, and Chan Ghee Koh. "Prediction of Low Failure Probabilities With Application to Marine Risers." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61574.
Повний текст джерелаJimeno Yepes, Antonio, Jianbin Tang, and Benjamin Scott Mashford. "Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/274.
Повний текст джерелаWang, Yan. "System Resilience Quantification for Probabilistic Design of Internet-of-Things Architecture." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59426.
Повний текст джерелаKyriazis, A., A. Tsalavoutas, K. Mathioudakis, M. Bauer, and O. Johanssen. "Gas Turbine Fault Identification by Fusing Vibration Trending and Gas Path Analysis." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59942.
Повний текст джерелаGalkin, Andrii, Iryna Polchaninova, Olena Galkina, and Iryna Balandina. "Retail trade area analysis using multiple variables modeling at residential zone." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.041.
Повний текст джерелаHarlow, D. Gary. "Lower Tail Estimation of Fatigue Life." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93104.
Повний текст джерелаBorozdin, Sergey Olegovich, Anatoly Nikolaevich Dmitrievsky, Nikolai Alexandrovich Eremin, Alexey Igorevich Arkhipov, Alexander Georgievich Sboev, Olga Kimovna Chashchina-Semenova, and Leonid Konstantinovich Fitzner. "Drilling Problems Forecast Based on Neural Network." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30984-ms.
Повний текст джерела