Academic literature on the topic 'Estimation of probability density function'
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Journal articles on the topic "Estimation of probability density function"
Kay, S. "Model-based probability density function estimation." IEEE Signal Processing Letters 5, no. 12 (December 1998): 318–20. http://dx.doi.org/10.1109/97.735424.
Full textBALÁZS, FERENC, and SÁNDOR IMRE. "QUANTUM COMPUTATION BASED PROBABILITY DENSITY FUNCTION ESTIMATION." International Journal of Quantum Information 03, no. 01 (March 2005): 93–98. http://dx.doi.org/10.1142/s0219749905000578.
Full textChen, Song Xi. "Probability Density Function Estimation Using Gamma Kernels." Annals of the Institute of Statistical Mathematics 52, no. 3 (September 2000): 471–80. http://dx.doi.org/10.1023/a:1004165218295.
Full textLópez-Rubio, Ezequiel, and Juan Miguel Ortiz-de-Lazcano-Lobato. "Soft clustering for nonparametric probability density function estimation." Pattern Recognition Letters 29, no. 16 (December 2008): 2085–91. http://dx.doi.org/10.1016/j.patrec.2008.07.010.
Full textAlencar, M. S. "Estimation of probability density function using spectral analysis." Electronics Letters 34, no. 2 (1998): 150. http://dx.doi.org/10.1049/el:19980170.
Full textSrikanth, M., H. K. Kesavan, and P. H. Roe. "Probability density function estimation using the MinMax measure." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 30, no. 1 (2000): 77–83. http://dx.doi.org/10.1109/5326.827456.
Full textKwasniok, Frank. "Semiparametric maximum likelihood probability density estimation." PLOS ONE 16, no. 11 (November 9, 2021): e0259111. http://dx.doi.org/10.1371/journal.pone.0259111.
Full textXiang, Xiaojing. "Estimation of conditional quantile density function." Journal of Nonparametric Statistics 4, no. 3 (January 1995): 309–16. http://dx.doi.org/10.1080/10485259508832621.
Full textKraft, C. H., Y. Lepage, and C. Van Eeden. "Estimation of a symmetric density function." Communications in Statistics - Theory and Methods 14, no. 2 (January 1985): 273–88. http://dx.doi.org/10.1080/03610928508828911.
Full textOryshchenko, Vitaliy, and Richard J. Smith. "Improved density and distribution function estimation." Electronic Journal of Statistics 13, no. 2 (2019): 3943–84. http://dx.doi.org/10.1214/19-ejs1619.
Full textDissertations / Theses on the topic "Estimation of probability density function"
Joshi, Niranjan Bhaskar. "Non-parametric probability density function estimation for medical images." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:ebc6af07-770b-4fee-9dc9-5ebbe452a0c1.
Full textKharoufeh, Jeffrey P. "Density estimation for functions of correlated random variables." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177097417.
Full textHao, Wei-Da. "Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4587.
Full textSadeghi, Mohammad T. "Automatic architecture selection for probability density function estimation in computer vision." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843248/.
Full textPhillips, Kimberly Ann. "Probability Density Function Estimation Applied to Minimum Bit Error Rate Adaptive Filtering." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/33280.
Full textMaster of Science
Esterhuizen, Gerhard. "Generalised density function estimation using moments and the characteristic function." Thesis, Link to the online version, 2003. http://hdl.handle.net/10019.1/1001.
Full textSantos, André Duarte dos. "Implied probability density functions: Estimation using hypergeometric, spline and lognormal functions." Master's thesis, Instituto Superior de Economia e Gestão, 2011. http://hdl.handle.net/10400.5/3372.
Full textThis thesis examines the stability and accuracy of three different methods to estimate Risk-Neutral Density functions (RNDs) using European options. These methods are the Double-Lognormal Function (DLN), the Smoothed Implied Volatility Smile (SML) and the Density Functional Based on Confluent Hypergeometric function (DFCH). These methodologies were used to obtain the RNDs from the option prices with the underlying USDBRL (price of US dollars in terms of Brazilian reals) for different maturities (1, 3 and 6 months), and then tested in order to analyze which method best fits a simulated "true" world as estimated through the Heston model (accuracy measure) and which model has a better performance in terms of stability. We observed that in the majority of the cases the SML outperformed the DLN and DFCH in capturing the "true" implied skewness. The DFCH and DLN methods were better than the SML model at estimating the "true" Kurtosis. However, due to the higher sensitivity of the skewness and kurtosis measures to the tails of the distribution (all the information outside the available strike prices is extrapolated and the probability masses outside this range can have ininite forms) we also compared the tested models using the root mean integrated squared error (RMISE) which is less sensitive to the tails of the distribution. We observed that using the RMISE criteria, the DFCH outperformed the other methods as a better estimator of the "true" RND. Besides testing which model best captured the "true" world's expectations, we an¬alyzed the historical summary statistics of the RNDs obtained from the FX options on the USDBRL for the period between June 2006 (before the start of the subprime crisis) and February 2010 (seven months before the Brazilian general election).
Calatayud, Gregori Julia. "Computational methods for random differential equations: probability density function and estimation of the parameters." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/138396.
Full text[ES] Los modelos matemáticos basados en ecuaciones diferenciales deterministas no tienen en cuenta la incertidumbre inherente del fenómeno físico (en un sentido amplio) bajo estudio. Además, a menudo se producen inexactitudes en los datos recopilados debido a errores en las mediciones. Por lo tanto, es necesario tratar los parámetros de entrada del modelo como cantidades aleatorias, en forma de variables aleatorias o procesos estocásticos. Esto da lugar al estudio de las ecuaciones diferenciales aleatorias. El cálculo de la función de densidad de probabilidad de la solución estocástica es importante en la cuantificación de la incertidumbre de la respuesta del modelo. Aunque dicho cálculo es un objetivo difícil en general, ciertas expansiones estocásticas para los coeficientes del modelo dan lugar a representaciones fieles de la solución estocástica, lo que permite aproximar su función de densidad. En este sentido, las expansiones de Karhunen-Loève y de caos polinomial generalizado constituyen herramientas para dicha aproximación de la densidad. Además, los métodos basados en discretizaciones de esquemas numéricos de diferencias finitas permiten aproximar la solución estocástica, por lo tanto, su función de densidad de probabilidad. La parte principal de esta disertación tiene como objetivo aproximar la función de densidad de probabilidad de modelos matemáticos importantes con incertidumbre en su formulación. Concretamente, en esta memoria se estudian, en un sentido estocástico, los siguientes modelos que aparecen en diferentes áreas científicas: en Física, el modelo del péndulo amortiguado; en Biología y Epidemiología, los modelos de crecimiento logístico y de Bertalanffy, así como modelos de tipo epidemiológico; y en Termodinámica, la ecuación en derivadas parciales del calor. Utilizamos expansiones de Karhunen-Loève y de caos polinomial generalizado y esquemas de diferencias finitas para la aproximación de la densidad de la solución. Estas técnicas solo son aplicables cuando tenemos un modelo directo en el que los parámetros de entrada ya tienen determinadas distribuciones de probabilidad establecidas. Cuando los coeficientes del modelo se estiman a partir de los datos recopilados, tenemos un problema inverso. El enfoque de inferencia Bayesiana permite estimar la distribución de probabilidad de los parámetros del modelo a partir de su distribución de probabilidad previa y la verosimilitud de los datos. La cuantificación de la incertidumbre para la respuesta del modelo se lleva a cabo utilizando la distribución predictiva a posteriori. En este sentido, la última parte de la tesis muestra la estimación de las distribuciones de los parámetros del modelo a partir de datos experimentales sobre el crecimiento de bacterias. Para hacerlo, se utiliza un método híbrido que combina la estimación de parámetros Bayesianos y los desarrollos de caos polinomial generalizado.
[CAT] Els models matemàtics basats en equacions diferencials deterministes no tenen en compte la incertesa inherent al fenomen físic (en un sentit ampli) sota estudi. A més a més, sovint es produeixen inexactituds en les dades recollides a causa d'errors de mesurament. Es fa així necessari tractar els paràmetres d'entrada del model com a quantitats aleatòries, en forma de variables aleatòries o processos estocàstics. Açò dóna lloc a l'estudi de les equacions diferencials aleatòries. El càlcul de la funció de densitat de probabilitat de la solució estocàstica és important per a quantificar la incertesa de la sortida del model. Tot i que, en general, aquest càlcul és un objectiu difícil d'assolir, certes expansions estocàstiques dels coeficients del model donen lloc a representacions fidels de la solució estocàstica, el que permet aproximar la seua funció de densitat. En aquest sentit, les expansions de Karhunen-Loève i de caos polinomial generalitzat esdevenen eines per a l'esmentada aproximació de la densitat. A més a més, els mètodes basats en discretitzacions mitjançant esquemes numèrics de diferències finites permeten aproximar la solució estocàstica, per tant la seua funció de densitat de probabilitat. La part principal d'aquesta dissertació té com a objectiu aproximar la funció de densitat de probabilitat d'importants models matemàtics amb incerteses en la seua formulació. Concretament, en aquesta memòria s'estudien, en un sentit estocàstic, els següents models que apareixen en diferents àrees científiques: en Física, el model del pèndol amortit; en Biologia i Epidemiologia, els models de creixement logístic i de Bertalanffy, així com models de tipus epidemiològic; i en Termodinàmica, l'equació en derivades parcials de la calor. Per a l'aproximació de la densitat de la solució, ens basem en expansions de Karhunen-Loève i de caos polinomial generalitzat i en esquemes de diferències finites. Aquestes tècniques només són aplicables quan tenim un model cap avant en què els paràmetres d'entrada tenen ja determinades distribucions de probabilitat. Quan els coeficients del model s'estimen a partir de les dades recollides, tenim un problema invers. L'enfocament de la inferència Bayesiana permet estimar la distribució de probabilitat dels paràmetres del model a partir de la seua distribució de probabilitat prèvia i la versemblança de les dades. La quantificació de la incertesa per a la resposta del model es fa mitjançant la distribució predictiva a posteriori. En aquest sentit, l'última part de la tesi mostra l'estimació de les distribucions dels paràmetres del model a partir de dades experimentals sobre el creixement de bacteris. Per a fer-ho, s'utilitza un mètode híbrid que combina l'estimació de paràmetres Bayesiana i els desenvolupaments de caos polinomial generalitzat.
This work has been supported by the Spanish Ministerio de Econom´ıa y Competitividad grant MTM2017–89664–P.
Calatayud Gregori, J. (2020). Computational methods for random differential equations: probability density function and estimation of the parameters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138396
TESIS
Premiado
Rahikainen, I. (Ilkka). "Direct methodology for estimating the risk neutral probability density function." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201404241289.
Full textHeinemann, Christian [Verfasser]. "Estimation and regularization of probability density functions in image processing / Christian Heinemann." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1058851497/34.
Full textBooks on the topic "Estimation of probability density function"
Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.
Find full textDevroye, Luc. Nonparametric density estimation: The L1 view. New York: Wiley, 1985.
Find full textLászló, Györfi, ed. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.
Find full textChurnside, 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.
Find full textR, Thompson James. Nonparametric function estimation, modeling, and simulation. Philadelphia: Society for Industrial and Applied Mathematics, 1990.
Find full textYamazaki, Hidekatsu. Determination of wave height spectrum by means of a joint probability density function. College Station, Tex: Sea Grant College Program, Texas A & M University, 1985.
Find full textFornari, Fabio. Recovering the probability density function of asset prices using GARCH as diffusion approximations. [Roma]: Banca d'Italia, 2001.
Find full textFornari, Fabio. The probability density function of interest rates implied in the price of options. Rome: Banca d'Italia, 1998.
Find full textMa, Xiaofang. Computation of the probability density function and the cumulative distribution function of the generalized gamma variance model. Ottawa: National Library of Canada, 2002.
Find full textSimon, M. Steady-state probability density function of the phase error for a DPLL with an integrate-and-dump device. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1986.
Find full textBook chapters on the topic "Estimation of probability density function"
Diwekar, Urmila, and Amy David. "Probability Density Functions and Kernel Density Estimation." In SpringerBriefs in Optimization, 27–34. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2282-6_3.
Full textLópez-Rubio, Ezequiel, Juan Miguel Ortiz-de-Lazcano-Lobato, and María Carmen Vargas-González. "Nonparametric Location Estimation for Probability Density Function Learning." In Lecture Notes in Computer Science, 106–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02478-8_14.
Full textLópez-Rubio, Ezequiel, Juan Miguel Ortiz-de-Lazcano-Lobato, Domingo López-Rodríguez, and María del Carmen Vargas-González. "Soft Clustering for Nonparametric Probability Density Function Estimation." In Lecture Notes in Computer Science, 707–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_72.
Full textGentle, James E. "Nonparametric Estimation of Probability Density Functions." In Statistics and Computing, 487–514. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98144-4_15.
Full textMurthy, K. S. R., and O. P. Rahi. "Wind Power Density Estimation Using Rayleigh Probability Distribution Function." In Advances in Intelligent Systems and Computing, 265–75. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_26.
Full textZarch, M. Ghaniee, Y. Alipouri, and J. Poshtan. "A Soft-Sensor Approach to Probability Density Function Estimation." In Integral Methods in Science and Engineering, 247–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16727-5_21.
Full textMimura, Yuta, Kazuhiro Hotta, and Haruhisa Takahashi. "Action Recognition Based on Non-parametric Probability Density Function Estimation." In Advances in Visual Computing, 489–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10520-3_46.
Full textLópez-Rubio, Ezequiel, Juan Miguel Ortiz-de-Lazcano-Lobato, Domingo López-Rodríguez, Enrique Mérida-Casermeiro, and María del Carmen Vargas-González. "Local Selection of Model Parameters in Probability Density Function Estimation." In Artificial Neural Networks – ICANN 2006, 292–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_30.
Full textGentle, James E. "Estimation of Probability Density Functions Using Parametric Models." In Statistics and Computing, 475–85. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98144-4_14.
Full textNadaraya, E. A. "Strongly Consistent in Functional Metrics Estimators of Probability Density." In Nonparametric Estimation of Probability Densities and Regression Curves, 42–61. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2583-0_3.
Full textConference papers on the topic "Estimation of probability density function"
Wu, Yingyan, Yulin He, and Joshua Zhexue Huang. "Clustering Ensembles Based on Probability Density Function Estimation." In 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2020. http://dx.doi.org/10.1109/cscloud-edgecom49738.2020.00029.
Full textKoroglu, Ozan, Feza Arikan, Nisa Turel, Melih S. Aysezen, and Muh Onur Lenk. "Estimation of Probability Density Function for TUSAGA TEC." In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU 2010). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5653362.
Full textChen, S., X. Hong, and C. J. Harris. "Probability Density Function Estimation Using Orthogonal Forward Regression." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371350.
Full textFahmy, Suhaib A. "Histogram-based probability density function estimation on FPGAs." In 2010 International Conference on Field-Programmable Technology (FPT). IEEE, 2010. http://dx.doi.org/10.1109/fpt.2010.5681457.
Full textSuga, Norisato, Kazuto Yano, Julian Webber, Yafei Hou, Toshihide Higashimori, and Yoshinori Suzuki. "Estimation of Probability Density Function Using Multi-bandwidth Kernel Density Estimation for Throughput." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065033.
Full textMukherjee, Arpita, and Uma Datta. "Real time probability density function estimation in sensor networks." In 2010 Sixth International Conference on Wireless Communication and Sensor Networks (WCSN). IEEE, 2010. http://dx.doi.org/10.1109/wcsn.2010.5712301.
Full textJing Wang, Xiaoling Li, and Jianhong Ni. "Probability density function estimation based on representative data samples." In IET International Conference on Communication Technology and Application (ICCTA 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0757.
Full textXie, Junhao, and Zexun Wang. "Probability Density Function Estimation Based on Windowed Fourier Transform of Characteristic Function." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5300813.
Full textMukherjee, A., and D. Mukherjee. "Distributed probability density function estimation of environmental function from sensor network data." In 2013 International Conference on Signal Processing, Image Processing, and Pattern Recognition (ICSIPR). IEEE, 2013. http://dx.doi.org/10.1109/icsipr.2013.6497993.
Full textDumitru, O., M. Mitrea, F. Prêteux, and A. Pathak. "Probability density function estimation for video in the DCT domain." In Electronic Imaging 2008, edited by Jaakko T. Astola, Karen O. Egiazarian, and Edward R. Dougherty. SPIE, 2008. http://dx.doi.org/10.1117/12.767469.
Full textReports on the topic "Estimation of probability density function"
Hao, Wei-Da. Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6471.
Full textIde, Kayo. Predictability and Ensemble Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada429618.
Full textIde, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630373.
Full textIde, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada624633.
Full textIde, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada625720.
Full textSmith, Richard J., and Vitaliy Oryshchenko. Improved density and distribution function estimation. The IFS, July 2018. http://dx.doi.org/10.1920/wp.cem.2018.4718.
Full textClark, G. Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/15011636.
Full textJordan, P. D., C. M. Oldenburg, and J. P. Nicot. Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation. Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1016011.
Full textChow, Winston C. Analysis of the Probability Density Function of the Monopulse Ratio Radar Signal. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada315600.
Full textDESJARDIN, PAUL E., MELVIN R. BAER, RAYMOND L. BELL, and EUGENE S. HERTEL, JR. Towards Numerical Simulation of Shock Induced Combustion Using Probability Density Function Approaches. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/801388.
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