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Journal articles on the topic 'Densité de probabilités'

Author: Grafiati

Published: 8 June 2024

Last updated: 19 July 2025

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1

Riopel, Martin, Jean Bégin, and 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, no. 7 (2010): 1458–72. http://dx.doi.org/10.1139/x10-059.

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La coupe avec protection des petites tiges marchandes est un type de coupe partielle qui consiste généralement à récolter toutes les tiges d’un diamètre à hauteur de poitrine (dhp) supérieur à 15,0 cm, tout en conservant les tiges de plus petites dimensions. Le succès du traitement, appliqué à des forêts résineuses mûres dominées par le sapin baumier ( Abies balsamea (L.) Mill.) ou l’épinette noire ( Picea mariana (Mill.) Britton, Sterns & Poggenb.), repose en partie sur la capacité des tiges protégées à survivre. Un modèle logistique mixte a été calibré à partir de 27 blocs expérimentaux

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2

Ouimet, Marc, and Pierre Tremblay. "Trajets urbains et risques de victimisation : les sites de transit et le cas du métro de Montréal." Criminologie 34, no. 1 (2002): 157–76. http://dx.doi.org/10.7202/004759ar.

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Résumé Les acteurs urbains sont généralement en mouvement et cessent de l'être seulement lorsque leurs activités exigent d'eux qu'ils demeurent stationnaires pour un intervalle de temps limité. Leurs parcours, composé de sommets (destinations) reliés entre eux par des chemins, forme un circuit, chaque trajectoire ramenant le plus souvent la personne qui se déplace à son point d'origine. Nous analysons la distribution des probabilités individuelles de victimisation personnelle associées aux diverses destinations qui définissent ce parcours (lieux de magasinage, de loisir, de vie domestique et d

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3

Assis, Janilson Pinheiro, Roberto Pequeno de Sousa, Bem Deivid de Oliveira Batista, and Paulo César Ferreira Linhares. "Probabilidade de chuva em Piracicaba, SP, através da distribuição densidade de probabilidade Gama." Revista Brasileira de Geografia Física 11, no. 2 (2018): 814–25. http://dx.doi.org/10.26848/rbgf.v10.6.p814-825.

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4

Assis, Janilson Pinheiro, Roberto Pequeno de Sousa, Bem Deivid de Oliveira Batista, and Paulo César Ferreira Linhares. "Probabilidade de chuva em Piracicaba, SP, através da distribuição densidade de probabilidade Gama." Revista Brasileira de Geografia Física 11, no. 3 (2018): 814–25. http://dx.doi.org/10.26848/rbgf.v11.3.p814-825.

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5

Farmer, Jenny, Eve Allen, and Donald J. Jacobs. "Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities." Mathematics 11, no. 1 (2022): 155. http://dx.doi.org/10.3390/math11010155.

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Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional condit

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6

Bian Chenshu, 边宸舒, 刘元坤 Liu Yuankun та 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, № 7 (2022): 0712002. http://dx.doi.org/10.3788/aos202242.0712002.

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7

Jones, M. C., and F. Daly. "Density probability plots." Communications in Statistics - Simulation and Computation 24, no. 4 (1995): 911–27. http://dx.doi.org/10.1080/03610919508813284.

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8

Xiao, Yongshun. "THE MARGINAL PROBABILITY DENSITY FUNCTIONS OF WISHART PROBABILITY DENSITY FUNCTION." Far East Journal of Theoretical Statistics 54, no. 3 (2018): 239–326. http://dx.doi.org/10.17654/ts054030239.

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9

Lin, Yi-Shin, Andrew Heathcote, and William R. Holmes. "Parallel probability density approximation." Behavior Research Methods 51, no. 6 (2019): 2777–99. http://dx.doi.org/10.3758/s13428-018-1153-1.

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10

Amonmidé, Isidore, Germain D. Fayalo, and 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 (August 31, 2020): 15676–97. http://dx.doi.org/10.35759/jabs.152.7.

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Objectif : L’objectif de l’étude était d’identifier les meilleures périodes et densités de semis dans les différentes zones agro-écologiques cotonnières du Bénin dans un contexte de changement climatique. Méthodologie et résultats : Les expérimentations ont été conduites pendant deux ans (2017 et 2018) en station au Bénin dans un dispositif expérimental en split-plot à deux facteurs, la période (facteur principal) et la densité de semis (facteur secondaire) respectivement à quatre et cinq variantes avec quatre répétitions. Les données collectées ont été soumises à une analyse de variance sous

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11

Zhong-hua, Liu, Chen Li-hua, and Zhu You-xing. "Probability Density and Joint Probability Density of SDE with General Nonlinear Gaussian Noise." Communications in Theoretical Physics 13, no. 2 (1990): 135–46. http://dx.doi.org/10.1088/0253-6102/13/2/135.

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12

Rigollet, Philippe. "Inégalités d'oracle pour l'estimation d'une densité de probabilité." Comptes Rendus Mathematique 340, no. 1 (2005): 59–62. http://dx.doi.org/10.1016/j.crma.2004.11.009.

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13

Kohnle, Antje, Alexander Jackson, and Mark Paetkau. "The Difference Between a Probability and a Probability Density." Physics Teacher 57, no. 3 (2019): 190–92. http://dx.doi.org/10.1119/1.5092484.

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14

Anju, Dr Vineeta Basotia, and Dr Ritikesh Kumar. "Analysis on Probability Mass Function and Probability Density Function." Irish Interdisciplinary Journal of Science & Research 08, no. 01 (2024): 08–12. http://dx.doi.org/10.46759/iijsr.2024.8102.

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Probability Mass Function (PMF) and Probability Density Function (PDF) are fundamental concepts in probability theory and statistics that play a crucial role in describing the probability distribution of random variables. This abstract provides a comprehensive overview of these concepts, highlighting their definitions, characteristics, and applications. The Probability Mass Function is a concept primarily associated with discrete random variables. It defines the probability of a specific outcome occurring. The PMF assigns probabilities to individual values in the sample space, providing a clea

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15

Maschio, Samuele. "Natural density and probability, constructively." Reports on Mathematical Logic 55 (2020): 41–59. http://dx.doi.org/10.4467/20842589rm.20.002.12434.

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16

Koliander, Gunther, Yousef El-Laham, Petar M. Djuric, and Franz Hlawatsch. "Fusion of Probability Density Functions." Proceedings of the IEEE 110, no. 4 (2022): 404–53. http://dx.doi.org/10.1109/jproc.2022.3154399.

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17

Abdul-al, Khaled I., and J. L. Geluk. "ON SMOOTHED PROBABILITY DENSITY ESTIMATION." Bulletin of informatics and cybernetics 23, no. 3/4 (1989): 199–208. http://dx.doi.org/10.5109/13406.

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18

Kakizawa, Yoshihide. "Bernstein polynomial probability density estimation." Journal of Nonparametric Statistics 16, no. 5 (2004): 709–29. http://dx.doi.org/10.1080/1048525042000191486.

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19

Minotti, F. O., and C. Ferro Fontán. "Navier-stokes probability density function." European Journal of Mechanics - B/Fluids 17, no. 4 (1998): 505–18. http://dx.doi.org/10.1016/s0997-7546(98)80007-1.

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20

Waissi, Gary R. "A unifying probability density function." Applied Mathematics Letters 6, no. 5 (1993): 25–26. http://dx.doi.org/10.1016/0893-9659(93)90093-3.

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21

Rosenbrock, H. H. "The quantum-mechanical probability density." Physics Letters A 116, no. 9 (1986): 410–12. http://dx.doi.org/10.1016/0375-9601(86)90370-1.

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22

Cox, Nicholas J. "Speaking Stata: Density Probability Plots." Stata Journal: Promoting communications on statistics and Stata 5, no. 2 (2005): 259–73. http://dx.doi.org/10.1177/1536867x0500500210.

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Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample's order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated a

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23

Stephens, M. E., B. W. Goodwin, and T. H. Andres. "Deriving parameter probability density functions." Reliability Engineering & System Safety 42, no. 2-3 (1993): 271–91. http://dx.doi.org/10.1016/0951-8320(93)90094-f.

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24

Ashby, F. Gregory, and Leola A. Alfonso-Reese. "Categorization as Probability Density Estimation." Journal of Mathematical Psychology 39, no. 2 (1995): 216–33. http://dx.doi.org/10.1006/jmps.1995.1021.

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25

Carta, Lynn, and David Carta. "Nematode specific gravity profiles and applications to flotation extraction and taxonomy." Nematology 2, no. 2 (2000): 201–10. http://dx.doi.org/10.1163/156854100508935.

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AbstractA technique is described that refines the standard sugar flotation procedure used to isolate nematodes from their surroundings. By centrifuging nematodes in a number of increasing specific gravity solutions and plotting the fraction floating, the cumulative probability distribution of the population’s specific gravity is generated. By assuming normality, the population mean, μ, and standard deviation, σ, are found by a nonlinear least squares procedure. These density parameters along with their error covariance matrix may be used as a taxonomic physical character. A chi-squared test is

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26

McIntyre, T., T. L. Majelantle, D. J. Slip, and R. G. Harcourt. "Quantifying imperfect camera-trap detection probabilities: implications for density modelling." Wildlife Research 47, no. 2 (2020): 177. http://dx.doi.org/10.1071/wr19040.

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Abstract ContextData obtained from camera traps are increasingly used to inform various population-level models. Although acknowledged, imperfect detection probabilities within camera-trap detection zones are rarely taken into account when modelling animal densities. AimsWe aimed to identify parameters influencing camera-trap detection probabilities, and quantify their relative impacts, as well as explore the downstream implications of imperfect detection probabilities on population-density modelling. MethodsWe modelled the relationships between the detection probabilities of a standard camera

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27

Mejia, Hannah, and Jay Pulliam. "P‐ and T‐Axis Probabilities (PaTaPs): Characterizing Regional Stress Patterns with Probability Density Functions of Fault‐Plane Uncertainties." Seismological Research Letters 89, no. 6 (2018): 2354–61. http://dx.doi.org/10.1785/0220180112.

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28

Jamieson, L. E., and S. P. Brooks. "Density dependence in North American ducks." Animal Biodiversity and Conservation 27, no. 1 (2004): 113–28. http://dx.doi.org/10.32800/abc.2004.27.0113.

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The existence or otherwise of density dependence within a population can have important implications for the management of that population. Here, we use estimates of abundance obtained from annual aerial counts on the major breeding grounds of a variety of North American duck species and use a state space model to separate the observation and ecological system processes. This state space approach allows us to impose a density dependence structure upon the true underlying population rather than on the estimates and we demonstrate the improved robustness of this procedure for detecting density d

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29

Norets, Andriy, and Justinas Pelenis. "POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES." Econometric Theory 30, no. 3 (2013): 606–46. http://dx.doi.org/10.1017/s026646661300042x.

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This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strong

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30

Joachim, C. "The probability and energy density currents as density functionals." Journal of Physics A: Mathematical and General 19, no. 13 (1986): 2549–57. http://dx.doi.org/10.1088/0305-4470/19/13/020.

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31

Delgado, V. "Quantum probability distribution of arrival times and probability current density." Physical Review A 59, no. 2 (1999): 1010–20. http://dx.doi.org/10.1103/physreva.59.1010.

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32

Ooi, Hong, and Peter Hall. "Attributing a probability to the shape of a probability density." Annals of Statistics 32, no. 5 (2004): 2098–123. http://dx.doi.org/10.1214/009053604000000607.

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33

LIAN, Feng, Chong-Zhao HAN, Wei-Feng LIU, and Xiang-Hui YUAN. "Multiple-model Probability Hypothesis Density Smoother." Acta Automatica Sinica 36, no. 7 (2010): 939–50. http://dx.doi.org/10.3724/sp.j.1004.2010.00939.

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34

Kwasniok, Frank. "Semiparametric maximum likelihood probability density estimation." PLOS ONE 16, no. 11 (2021): e0259111. http://dx.doi.org/10.1371/journal.pone.0259111.

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A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global maximum likelihood without any roughness penalty. A statistically orthogonal formulation of the inference problem and a numerically stable and fast convex optimization algorithm for its solution are presented. Automatic model selection over the type and number of basis functions

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35

Vovan, Tai. "Cluster Width of probability Density functions." Intelligent Data Analysis 23, no. 2 (2019): 385–405. http://dx.doi.org/10.3233/ida-173794.

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36

Amindavar, H., and J. A. Ritcey. "Pade approximations of probability density functions." IEEE Transactions on Aerospace and Electronic Systems 30, no. 2 (1994): 416–24. http://dx.doi.org/10.1109/7.272264.

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37

Reggiani, L., and G. Tartara. "Probability density functions of soft information." IEEE Communications Letters 6, no. 2 (2002): 52–54. http://dx.doi.org/10.1109/4234.984688.

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38

Gille, Sarah T., and Stefan G. Llewellyn Smith. "Velocity Probability Density Functions from Altimetry." Journal of Physical Oceanography 30, no. 1 (2000): 125–36. http://dx.doi.org/10.1175/1520-0485(2000)030<0125:vpdffa>2.0.co;2.

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39

Miller, Gad, and David Horn. "Probability Density Estimation Using Entropy Maximization." Neural Computation 10, no. 7 (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,

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40

Campos Venuti, L., and P. Zanardi. "Probability density of quantum expectation values." Physics Letters A 377, no. 31-33 (2013): 1854–61. http://dx.doi.org/10.1016/j.physleta.2013.05.041.

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41

Arranz, F. J., F. Borondo, and R. M. Benito. "Probability density distributions in phase space." Journal of Molecular Structure: THEOCHEM 426, no. 1-3 (1998): 87–93. http://dx.doi.org/10.1016/s0166-1280(97)00314-x.

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42

Ortgies, G. "Probability density function of amplitude scintillations." Electronics Letters 21, no. 4 (1985): 141. http://dx.doi.org/10.1049/el:19850100.

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43

Campioni, Luca, and Paolo Vestrucci. "On system failure probability density function." Reliability Engineering & System Safety 92, no. 10 (2007): 1321–27. http://dx.doi.org/10.1016/j.ress.2006.09.002.

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44

Morad, Kamalaldin, William Y. Svrcek, and Ian McKay. "Probability density estimation using incomplete data." ISA Transactions 39, no. 4 (2000): 379–99. http://dx.doi.org/10.1016/s0019-0578(00)00016-1.

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45

Kay, S. "Model-based probability density function estimation." IEEE Signal Processing Letters 5, no. 12 (1998): 318–20. http://dx.doi.org/10.1109/97.735424.

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46

Farmer, Jenny, and Donald Jacobs. "High throughput nonparametric probability density estimation." PLOS ONE 13, no. 5 (2018): e0196937. http://dx.doi.org/10.1371/journal.pone.0196937.

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47

Yanev, Toni K. "Probability density functions of vegetation indices." Acta Astronautica 26, no. 2 (1992): 85–91. http://dx.doi.org/10.1016/0094-5765(92)90049-o.

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48

Sithiravel, Rajiv, Xin Chen, Ratnasingham Tharmarasa, Bhashyam Balaji, and Thiagalingam Kirubarajan. "The Spline Probability Hypothesis Density Filter." IEEE Transactions on Signal Processing 61, no. 24 (2013): 6188–203. http://dx.doi.org/10.1109/tsp.2013.2284139.

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49

Mahler, R. P. S., Ba-Tuong Vo, and Ba-Ngu Vo. "Forward-Backward Probability Hypothesis Density Smoothing." IEEE Transactions on Aerospace and Electronic Systems 48, no. 1 (2012): 707–28. http://dx.doi.org/10.1109/taes.2012.6129665.

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50

Schikora, Marek, Amadou Gning, Lyudmila Mihaylova, Daniel Cremers, and Wolfgang Koch. "Box-particle probability hypothesis density filtering." IEEE Transactions on Aerospace and Electronic Systems 50, no. 3 (2014): 1660–72. http://dx.doi.org/10.1109/taes.2014.120238.

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