Literatura académica sobre el tema "Densité de probabilités"
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Artículos de revistas sobre el tema "Densité de probabilités"
Riopel, Martin, Jean Bégin y Jean-Claude Ruel. "Probabilités de pertes des tiges individuelles, cinq ans après des coupes avec protection des petites tiges marchandes, dans des forêts résineuses du Québec". Canadian Journal of Forest Research 40, n.º 7 (julio de 2010): 1458–72. http://dx.doi.org/10.1139/x10-059.
Texto completoOuimet, Marc y Pierre Tremblay. "Trajets urbains et risques de victimisation : les sites de transit et le cas du métro de Montréal". Criminologie 34, n.º 1 (2 de octubre de 2002): 157–76. http://dx.doi.org/10.7202/004759ar.
Texto completoAssis, Janilson Pinheiro, Roberto Pequeno de Sousa, Bem Deivid de Oliveira Batista y Paulo César Ferreira Linhares. "Probabilidade de chuva em Piracicaba, SP, através da distribuição densidade de probabilidade Gama". Revista Brasileira de Geografia Física 11, n.º 2 (2018): 814–25. http://dx.doi.org/10.26848/rbgf.v10.6.p814-825.
Texto completoAssis, Janilson Pinheiro, Roberto Pequeno de Sousa, Bem Deivid de Oliveira Batista y Paulo César Ferreira Linhares. "Probabilidade de chuva em Piracicaba, SP, através da distribuição densidade de probabilidade Gama". Revista Brasileira de Geografia Física 11, n.º 3 (2018): 814–25. http://dx.doi.org/10.26848/rbgf.v11.3.p814-825.
Texto completoFarmer, Jenny, Eve Allen y Donald J. Jacobs. "Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities". Mathematics 11, n.º 1 (28 de diciembre de 2022): 155. http://dx.doi.org/10.3390/math11010155.
Texto completoBian Chenshu, 边宸舒, 刘元坤 Liu Yuankun y 于馨 Yu Xin. "基于概率密度函数的彩色相位测量轮廓术校正". Acta Optica Sinica 42, n.º 7 (2022): 0712002. http://dx.doi.org/10.3788/aos202242.0712002.
Texto completoJones, M. C. y F. Daly. "Density probability plots". Communications in Statistics - Simulation and Computation 24, n.º 4 (enero de 1995): 911–27. http://dx.doi.org/10.1080/03610919508813284.
Texto completoXiao, Yongshun. "THE MARGINAL PROBABILITY DENSITY FUNCTIONS OF WISHART PROBABILITY DENSITY FUNCTION". Far East Journal of Theoretical Statistics 54, n.º 3 (1 de mayo de 2018): 239–326. http://dx.doi.org/10.17654/ts054030239.
Texto completoLin, Yi-Shin, Andrew Heathcote y William R. Holmes. "Parallel probability density approximation". Behavior Research Methods 51, n.º 6 (30 de agosto de 2019): 2777–99. http://dx.doi.org/10.3758/s13428-018-1153-1.
Texto completoAmonmidé, Isidore, Germain D. Fayalo y Gustave D. Dagbenonbakin. "Effet de la période et densité de semis sur la croissance et le rendement du cotonnier au Bénin". Journal of Applied Biosciences 152 (31 de agosto de 2020): 15676–97. http://dx.doi.org/10.35759/jabs.152.7.
Texto completoTesis sobre el tema "Densité de probabilités"
Yode, Armel Fabrice Evrard. "Estimation de la densité de probabilité multidimensionnelle : risques minimax avec normalisation aléatoire et test d'indépendance". Aix-Marseille 1, 2004. http://www.theses.fr/2004AIX11002.
Texto completoIn the context of minimax theory, a new approach allowing one to improve the accuracy of estimation has been proposed by Lepski (1999). This approach which is a combination of adaptive estimation and hypothesis testing introduces a new kind of risks normalized by random variable depending on the observation. It implies construction of estimator attaining rate depending on observation. This estimator can be adaptive and the rate is better than minimax rate of convergence. In this thesis, we apply this theory to the problem of estimation of multidi-mensionnal probability density under independence hypothesis. Our work consists of two parts:- Independence test. We propose a new nonparametric independence test via minimax approach. The alternatives sets are described by L2-norm. We are interested in the study for tests for which the error of the first type can decrease to 0 as the number of observations increases. - Minimax risks with random normalizing factors. We construct estimator attaining random rate which is better than mini-max rate of convergence. Under independence hypothesis, this estimator can be adaptive
Hamon, Abdellatif. "Estimation d'une densité de probabilité multidimensionnelle par dualité". Rouen, 2000. http://www.theses.fr/2000ROUES055.
Texto completoRosa, Vargas José Ismäel de la. "Estimation de la densité de probabilité d'une mesure dans un cadre non-linéaire, non-gaussien". Paris 11, 2002. http://www.theses.fr/2002PA112201.
Texto completoThe characterization and modeling of an indirect measurement procedure is led by a set of previously observed data. The modeling task is it self a complex procedure which is correlated with the measurement objective. Far from model building and model selection, a theoretical and practical problem persists: What is the correct probability density function (PDF) of a parametric model? Once this PDF is approximated, the next step is to establish a mechanism to propagate this statistical information until the quantity of interest. In fact, such a quantity is a measurement estimate and it is a nonlinear function of the parametric model. The present work proposes some different methods to make statistical inferences about the measurement estimate. We propose a first approach based on bootstrap methods. Such methods are classical in statistical simulation together with Monte Carlo methods, and they require a significative time of calcul. However, the precision over the measurement PDF estimated by these methods is very good. On the other hand, we have verified that the bootstrap methods convergence is faster than the Primitive Monte Carlo's one. Another advantage of bootstrap is its capacity to determine the statistical nature of errors which perturb the measurement system. This is doing thanks to the empirical estimation of the errors PDF. The bootstrap convergence optimization could be achieved by smoothing the residuals or by using a modified iterated bootstrap scheme. More over, we propose to use robust estimation when outliers are present. The second approach is based on other sampling techniques called Markov Chain Monte Carlo (MCMC), the statistical inference obtained when using these methods is very interesting, since we can use all a priori information about the measurement system. We can reformulate the problem solution by using the Bayes rule. The Gibbs sampling and the Metropolis-Hastings algorithms were exploited in this work. We overcome to the MCMC convergence optimization problem by using a weighted resampling and coupling from the past (CFTP) schemes, moreover, we adapt such techniques to the measurement PDF approximation. The last proposed approach is based on the use of kernel methods. The main idea is founded on the nonparametric estimation of the errors PDF, since it is supposed unknown. Then, we optimize a criterion function based on the entropy of the errors' PDF, thus we obtain a minimum entropy estimator (MEE). The simulation of this estimation process by means of Monte Carlo, MCMC, or weighted bootstrap could led to us to construct a statistical approximation of the measurement population. .
Nehme, Bilal. "Techniques non-additives d'estimation de la densité de probabilité". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2010. http://tel.archives-ouvertes.fr/tel-00576957.
Texto completoDerouet, Charlotte. "La fonction de densité au carrefour entre probabilités et analyse en terminale S : Etude de la conception et de la mise en oeuvre de tâches d'introduction articulant lois à densité et calcul intégral". Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCC126/document.
Texto completoThis thesis focuses on the connections between probability and analysis (calculus) in the scientific track of Grade 12 (French baccalaureate program). We explored the ways in which links between the mathematics subfields of continuous probability and integral calculus are created and explored, through a research focused on the concept of density function. Using the Mathematical Working Space model and some elements of Activity Theory, we sought to identify tasks that would allow introducing this concept and building the semiotic relationship between probability and integral. In order to address this issue, we began with an epistemological and historical study of the birth of the concept of density function, which enabled us to identify the important role of statistics in this genesis. Then, an analysis of institutional documents and textbooks showed that the link between continuous probability and integral calculus is imposed on students and rarely exploited in the different tasks given to them. Finally, we studied the design and implementation of original introductory tasks through a research methodology that we call “collaborative didactic engineering”. The goal of these tasks is to get the class “collective” to construct the concept of density function and trigger the need for calculating areas under a curve. We highlighted the activities of the class “collective” in the construction of this notion by analyzing articulations between the three subfields: continuous probability, descriptive statistics and integral calculus
Akil, Nicolas. "Etude des incertitudes des modèles neuronaux sur la prévision hydrogéologique. Application à des bassins versants de typologies différentes". Electronic Thesis or Diss., IMT Mines Alès, 2021. http://www.theses.fr/2021EMAL0005.
Texto completoFloods and droughts are the two main risks in France and require a special attention. In these conditions, where climate change generates increasingly frequent extreme phenomena, modeling these risks is an essential element for water resource management.Currently, discharges and water heights are mainly predicted from physical or conceptual based models. Although efficient and necessary, the calibration and implementation of these models require long and costly studies.Hydrogeological forecasting models often use data from incomplete or poorly dimensioned measurement networks. Moreover, the behavior of the study basins is in most cases difficult to understand. This difficulty is thus noted to estimate the uncertainties associated with hydrogeological modeling.In this context, this thesis, supported by IMT Mines Alès and financed by the company aQuasys and ANRT, aims at developing models based on the systemic paradigm. These models require only basic knowledge on the physical characterization of the studied basin, and can be calibrated from only input and output information (rainfall and discharge/height).The most widely used models in the environmental world are neural networks, which are used in this project. This thesis seeks to address three main goals:1. Development of a model design method adapted to different variables (surface water flows/height) and to very different types of basins: watersheds or hydrogeological basins (groundwater height)2. Evaluation of the uncertainties associated with these models in relation to the types of targeted basins3. Reducing of these uncertaintiesSeveral basins are used to address these issues: the Blavet basin in Brittany and the basin of the Southern and Central Champagne Chalk groundwater table
Lerasle, Matthieu. "Rééchantillonnage et sélection de modèles optimale pour l'estimation de la densité". Toulouse, INSA, 2009. http://eprint.insa-toulouse.fr/archive/00000290/.
Texto completoPastel, Rudy. "Estimation de probabilités d'évènements rares et de quantiles extrêmes : applications dans le domaine aérospatial". Phd thesis, Université Européenne de Bretagne, 2012. http://tel.archives-ouvertes.fr/tel-00728108.
Texto completoQuiroz, Martínez Benjamín. "Étude de la variabilité temporelle et spatiale des peuplements des annélides polychètes de l'Atlantique nord-est européen, dynamique des peuplements en Manche et patrons de distribution sur le plateau continental". Thesis, Lille 1, 2010. http://www.theses.fr/2010LIL10106/document.
Texto completoOne of the key features of environmental field studies is their high variability at many different time and space scales. Because of these external influences and of the stochasticity introduced by the reproduction, population dynamics are also characterised by high variability over time and space. The search for universal scaling laws in ecology often involves considering a form of power-law distribution, power laws can emerge in population dynamics or in patterns of abundance, distribution, and richness. Using the polychaetes, group that colonises a large range of soft and hard marine sediment habitats, from intertidal to hadal zones, and are considered to be good surrogates to identify the main environmental conditions that control the structure and functioning of benthic communities, we try to identify the spatiotemporal changes in biodiversity for this characteristic benthic group. First, we discuss the dynamics of polychaete populations. Based on long-term series of three soft-bottom communities, we study the dynamics of polychaete populations using different statistical techniques; we characterise extreme events in abundance data and we show how to apply some quantification methods to highly erratic and intermittent biological series. Then, we discuss the spatial distribution of polychaete species aiming to: identify latitudinal, longitudinal and bathymetric patterns on the European northeast Atlantic continental shelf; and test the existence of general, perhaps universal, patterns for characterising biodiversity i.e. increasing diversity with sampled area, its decay from the equator to the poles and the increase in richness with the total abundance of individuals
Bordet, Nicolas. "Modélisation 0D/1D de la combustion diesel : du mode conventionnel au mode homogène". Phd thesis, Université d'Orléans, 2011. http://tel.archives-ouvertes.fr/tel-00717396.
Texto completoLibros sobre el tema "Densité de probabilités"
Number theoretic density and logical limit laws. Providence, RI: American Mathematical Society, 2001.
Buscar texto completoUnited States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Probability density functions in turbulent channel flow. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.
Buscar texto completoUnited States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Probability density functions in turbulent channel flow. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.
Buscar texto completoPost-fragmentation probability density for bacterial aggregates. [Place of publication not identified]: Proquest, Umi Dissertatio, 2012.
Buscar texto completoOn four approaches to density. Frankfurt am Main: Peter Lang, 2013.
Buscar texto completoRonald, Johnson L., Smith P. L y United States. National Aeronautics and Space Administration., eds. Probability density functions of observed rainfall in Montana. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoRonald, Johnson L., Smith P. L y United States. National Aeronautics and Space Administration., eds. Probability density functions of observed rainfall in Montana. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoChurnside, James H. Probability density function of optical scintillations (scintillation distribution). Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1989.
Buscar texto completoJ, Lataitis R. y Wave Propagation Laboratory, eds. Probability density function of optical scintillations (scintillation distribution). Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Wave Propagation Laboratory, 1989.
Buscar texto completoLászló, Györfi, ed. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.
Buscar texto completoCapítulos de libros sobre el tema "Densité de probabilités"
Gu, Chong. "Probability Density Estimation". En Smoothing Spline ANOVA Models, 177–210. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3683-0_6.
Texto completoGooch, Jan W. "Probability Density Function". En Encyclopedic Dictionary of Polymers, 992. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15330.
Texto completoGooch, Jan W. "Probability Density, ψ2". En Encyclopedic Dictionary of Polymers, 590. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9465.
Texto completoGooch, Jan W. "Probability Density Function". En Encyclopedic Dictionary of Polymers, 590. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9466.
Texto completoCastaing, B. "Probability Density Functions". En Turbulence, 81–85. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2586-8_13.
Texto completoRizzo, Maria L. "Probability Density Estimation". En Statistical Computing with R, 337–74. Second edition. | Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192760-12.
Texto completoNascimento, Abraão D. C. "Probability Density Function". En Encyclopedia of Mathematical Geosciences, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_257-2.
Texto completoNascimento, Abraão D. C. "Probability Density Function". En Encyclopedia of Mathematical Geosciences, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_257-1.
Texto completoGu, Chong. "Probability Density Estimation". En Smoothing Spline ANOVA Models, 237–84. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5369-7_7.
Texto completoNascimento, Abraão D. C. "Probability Density Function". En Encyclopedia of Mathematical Geosciences, 1112–16. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_257.
Texto completoActas de conferencias sobre el tema "Densité de probabilités"
Oskolkov, K. I. "The Schrödinger density and the Talbot effect". En Approximation and Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2006. http://dx.doi.org/10.4064/bc72-0-13.
Texto completoSun, Yi-Chieh y Inseok Hwang. "Gaussian Mixture Probability Hypothesis Density Filter with State-Dependent Probabilities". En 2021 European Control Conference (ECC). IEEE, 2021. http://dx.doi.org/10.23919/ecc54610.2021.9655137.
Texto completoLeifer, M. S. "Conditional Density Operators and the Subjectivity of Quantum Operations". En FOUNDATIONS OF PROBABILITY AND PHYSICS - 4. AIP, 2007. http://dx.doi.org/10.1063/1.2713456.
Texto completoAndreev, V. "The Reduction of Density Matrix and Measurement of Bell-CHSH Inequalities". En FOUNDATIONS OF PROBABILITY AND PHYSICS - 4. AIP, 2007. http://dx.doi.org/10.1063/1.2713465.
Texto completoWu, M., D. Zheng, J. Yuan, S. Zhang, A. Chen y B. Cheng. "Probability hypothesis density filter with low detection probability". En IET International Radar Conference (IET IRC 2020). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.0664.
Texto completoGarcia-Fernandez, Angel F. y Lennart Svensson. "Trajectory probability hypothesis density filter". En 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455270.
Texto completoErdinc, Ozgur, Peter Willett y Yaakov Bar-Shalom. "A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters". En Defense and Security Symposium, editado por Oliver E. Drummond. SPIE, 2006. http://dx.doi.org/10.1117/12.673194.
Texto completoSithiravel, Rajiv, Ratnasingham Tharmarasa, Mike McDonald, Michel Pelletier y Thiagalingam Kirubarajan. "The spline probability hypothesis density filter". En SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.921022.
Texto completoMeyers, Ronald E. "Quantum probability density function (QPDF) method". En Optics & Photonics 2005, editado por Ronald E. Meyers y Yanhua Shih. SPIE, 2005. http://dx.doi.org/10.1117/12.620152.
Texto completoXiaoyun, Teng, Yuan Jia y Yu Hongyi. "Probability density estimation based on SVM". En 2009 Global Mobile Congress. IEEE, 2009. http://dx.doi.org/10.1109/gmc.2009.5295893.
Texto completoInformes sobre el tema "Densité de probabilités"
Gaglianone, Wagner Piazza y Waldyr Dutra Areosa. Financial Conditions Indicator for Brazil. Inter-American Development Bank, agosto de 2017. http://dx.doi.org/10.18235/0011805.
Texto completoClark, G. Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging. Office of Scientific and Technical Information (OSTI), septiembre de 2004. http://dx.doi.org/10.2172/15011636.
Texto completoKitsul, Yuriy y Jonathan Wright. The Economics of Options-Implied Inflation Probability Density Functions. Cambridge, MA: National Bureau of Economic Research, junio de 2012. http://dx.doi.org/10.3386/w18195.
Texto completoKamrath, Matthew, D. Wilson, Carl Hart, Daniel Breton y Caitlin Haedrich. Evaluating parametric probability density functions for urban acoustic noise. Engineer Research and Development Center (U.S.), septiembre de 2020. http://dx.doi.org/10.21079/11681/38006.
Texto completoClark, Todd E., Gergely Ganics y Elmar Mertens. What is the predictive value of SPF point and density forecasts? Federal Reserve Bank of Cleveland, noviembre de 2022. http://dx.doi.org/10.26509/frbc-wp-202237.
Texto completoJordan, P. D., C. M. Oldenburg y J. P. Nicot. Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation. Office of Scientific and Technical Information (OSTI), mayo de 2011. http://dx.doi.org/10.2172/1016011.
Texto completoHao, Wei-Da. Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.6471.
Texto completoDESJARDIN, PAUL E., MELVIN R. BAER, RAYMOND L. BELL y EUGENE S. HERTEL, JR. Towards Numerical Simulation of Shock Induced Combustion Using Probability Density Function Approaches. Office of Scientific and Technical Information (OSTI), julio de 2002. http://dx.doi.org/10.2172/801388.
Texto completoChow, Winston C. Analysis of the Probability Density Function of the Monopulse Ratio Radar Signal. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1996. http://dx.doi.org/10.21236/ada315600.
Texto completoPoppeliers, Christian y Leiph Preston. An efficient method to estimate the probability density of seismic Green's functions. Office of Scientific and Technical Information (OSTI), agosto de 2021. http://dx.doi.org/10.2172/1813651.
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