Academic literature on the topic 'Probability density function'

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Journal articles on the topic "Probability density function"

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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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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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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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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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Ben Nakhi, Y., and S. L. Kalla. "A generalized beta function and associated probability density." International Journal of Mathematics and Mathematical Sciences 30, no. 8 (2002): 467–78. http://dx.doi.org/10.1155/s0161171202007512.

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We introduce and establish some properties of a generalized form of the beta function. Corresponding generalized incomplete beta functions are also defined. Moreover, we define a new probability density function (pdf) involving this new generalized beta function. Some basic functions associated with the pdf, such as moment generating function, mean residue function, and hazard rate function are derived. Some special cases are mentioned. Some figures for pdf, hazard rate function, and mean residue life function are given. These figures reflect the role of shape and scale parameters.
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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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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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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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Chen, Zhicheng, Yuequan Bao, Hui Li, and Billie F. Spencer. "A novel distribution regression approach for data loss compensation in structural health monitoring." Structural Health Monitoring 17, no. 6 (2017): 1473–90. http://dx.doi.org/10.1177/1475921717745719.

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Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using im
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Martinez, Alexandre Souto, Rodrigo Silva González, and César Augusto Sangaletti Terçariol. "Generalized Probability Functions." Advances in Mathematical Physics 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/206176.

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From the integration of nonsymmetrical hyperboles, a one-parameter generalization of the logarithmic function is obtained. Inverting this function, one obtains the generalized exponential function. Motivated by the mathematical curiosity, we show that these generalized functions are suitable to generalize some probability density functions (pdfs). A very reliable rank distribution can be conveniently described by the generalized exponential function. Finally, we turn the attention to the generalization of one- and two-tail stretched exponential functions. We obtain, as particular cases, the ge
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Dissertations / Theses on the topic "Probability density function"

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Pai, Madhusudan Gurpura. "Probability density function formalism for multiphase flows." [Ames, Iowa : Iowa State University], 2007.

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Louloudi, Sofia. "Transported probability density function : modelling of turbulent jet flames." Thesis, Imperial College London, 2003. http://hdl.handle.net/10044/1/8007.

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Aguirre-Saldivar, Rina Guadalupe. "Two scalar probability density function models for turbulent flames." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38213.

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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.

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The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computationally inefficient or need a large amount of training data. In this thesis, we critically analyse and f
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Hulek, Tomas. "Modelling of turbulent combustion using transported probability density function methods." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339223.

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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.

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The target of the study is to find out if the direct methodology could provide same information about the parameters of the risk neutral probability density function (RND) than the reference RND methodologies. The direct methodology is based on for defining the parameters of the RND from underlying asset by using futures contracts and only few at-the-money (ATM) and/or close at-the-money (ATM) options on asset. Of course for enabling the analysis of the feasibility of the direct methodology the reference RNDs must be estimated from the option data. Finally the results of estimating the paramet
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Kakhi, M. "The transported probability density function approach for predicting turbulent combusting flows." Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/8729.

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Sadeghi, Mohammad T. "Automatic architecture selection for probability density function estimation in computer vision." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843248/.

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In this thesis, the problem of probability density function estimation using finite mixture models is considered. Gaussian mixture modelling is used to provide a semi-parametric density estimate for a given data set. The fundamental problem with this approach is that the number of mixtures required to adequately describe the data is not known in advance. In this work, a predictive validation technique [91] is studied and developed as a useful, operational tool that automatically selects the number of components for Gaussian mixture models. The predictive validation test approves a candidate mo
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Weerasinghe, Weerasinghe Mudalige Sujith Rohitha. "Application of Lagrangian probability density function approach to turbulent reacting flows." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392476.

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Hao, Wei-Da. "Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4587.

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A new method for solving jitter noise in estimating high frequency waveform is proposed. It reduces the bias of the estimation in those points where all the other methods fail to achieve. It provides preliminary models for estimating percentiles in Normal, Exponential probability density function. Based on the model for Normal probability density function, a model for any probability density function is derived. The resulting percentiles, in turn, are used as estimates for the amplitude of the waveform. Simulation results show us with satisfactory accuracy.
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Books on the topic "Probability density function"

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Churnside, James H. Probability density function of optical scintillations (scintillation distribution). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1989.

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J, Lataitis R., and Wave Propagation Laboratory, eds. Probability density function of optical scintillations (scintillation distribution). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Wave Propagation Laboratory, 1989.

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Fornari, Fabio. Recovering the probability density function of asset prices using GARCH as diffusion approximations. Banca d'Italia, 2001.

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Center, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Yamazaki, Hidekatsu. Determination of wave height spectrum by means of a joint probability density function. Sea Grant College Program, Texas A & M University, 1985.

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Center, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Fornari, Fabio. The probability density function of interest rates implied in the price of options. Banca d'Italia, 1998.

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Ma, Xiaofang. Computation of the probability density function and the cumulative distribution function of the generalized gamma variance model. National Library of Canada, 2002.

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Simon, M. Steady-state probability density function of the phase error for a DPLL with an integrate-and-dump device. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1986.

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J, Mileant, and Jet Propulsion Laboratory (U.S.), eds. Steady-state probability density function of the phase error for a DPLL with an integrate-and-dump device. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1986.

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Book chapters on the topic "Probability density function"

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Gooch, Jan W. "Probability Density Function." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15330.

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Gooch, Jan W. "Probability Density Function." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9466.

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Nascimento, Abraão D. C. "Probability Density Function." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_257.

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Nascimento, Abraão D. C. "Probability Density Function." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_257-2.

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Nascimento, Abraão D. C. "Probability Density Function." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_257-1.

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Dohmen, Jos J., Theo G. J. Beelen, Oryna Dvortsova, E. Jan W. ter Maten, Bratislav Tasić, and Rick Janssen. "Calibration of Probability Density Function." In Mathematics in Industry. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30726-4_18.

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Suciu, Nicolae. "Probability and Filtered Density Function Approaches." In Diffusion in Random Fields. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15081-5_6.

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Gupta, A. K., and T. Varga. "Probability Density Function and Expected Values." In Elliptically Contoured Models in Statistics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1646-6_3.

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Bodschwinna, Horst, and Jörg Seewig. "Surface Statistics and Probability Density Function." In Encyclopedia of Tribology. Springer US, 2013. http://dx.doi.org/10.1007/978-0-387-92897-5_304.

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Figuera, Pau, Alfredo Cuzzocrea, and Pablo García Bringas. "Probability Density Function for Clustering Validation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40725-3_12.

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Conference papers on the topic "Probability density function"

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Zhang, Peize, and Xu Li. "Effectiveness of Normal Probability Density Function in Modelling Safety Score Distribution." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695416.

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Zhang, Jinfang, Ruoxuan Tian, and Di Wu. "Predictive Function Control of Output Probability Density Function." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623134.

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Meyers, Ronald E. "Quantum probability density function (QPDF) method." In Optics & Photonics 2005, edited by Ronald E. Meyers and Yanhua Shih. SPIE, 2005. http://dx.doi.org/10.1117/12.620152.

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Markhvida, Igor V., and Ludmila V. Chvyaleva. "Probability density function of speckle intensity crossing." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Dennis R. Pape. SPIE, 1994. http://dx.doi.org/10.1117/12.179116.

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Popov, Ivan A., Nikolay V. Sidorovsky, and Leonid M. Veselov. "Probability density function of non-Gaussian speckle." In Optoelectronic Science and Engineering '94: International Conference, edited by Wang Da-Heng, Anna Consortini, and James B. Breckinridge. SPIE, 1994. http://dx.doi.org/10.1117/12.182180.

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Ayala-Ramirez, Victor, Raul Sanchez-yanez, Oscar Ibarra-manzano, and Francisco Montecillo-puente. "Probability density function approximation using fuzzy rules." In 2006 Multiconference on Electronics and Photonics. IEEE, 2006. http://dx.doi.org/10.1109/mep.2006.335667.

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Fahmy, 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.

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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.

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Chen, 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.

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Koroglu, 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.

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Reports on the topic "Probability density function"

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Smith, Donald L., Denise Neudecker, and Roberto Capote Noy. Investigation of the Effects of Probability Density Function Kurtosis on Evaluated Data Results. IAEA Nuclear Data Section, 2018. http://dx.doi.org/10.61092/iaea.yxma-3y50.

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In two previous investigations that are documented in this IAEA report series, we examined the effects of non-Gaussian, non-symmetric probability density functions (PDFs) on the outcomes of data evaluations. Most of this earlier work involved considering just two independent input data values and their respective uncertainties. They were used to generate one evaluated data point. The input data are referred to, respectively, as the mean value and standard deviation pair (y0,s0) for a prior PDF p0(y) and a second mean value and standard deviation pair (ye,se) for a likelihood PDF pe(y). Concept
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Smith, Donald L., Denise Neudecker, and Roberto Capote Noy. Investigation of the Effects of Probability Density Function Kurtosis on Evaluated Data Results. IAEA Nuclear Data Section, 2020. http://dx.doi.org/10.61092/iaea.nqsh-f02d.

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In two previous investigations that are documented in this IAEA report series, we examined the effects of non-Gaussian, non-symmetric probability density functions (PDFs) on the outcomes of data evaluations. Most of this earlier work involved considering just two independent input data values and their respective uncertainties. They were used to generate one evaluated data point. The input data are referred to, respectively, as the mean value and standard deviation pair (y0,s0) for a prior PDF p0(y) and a second mean value and standard deviation pair (ye,se) for a likelihood PDF pe(y). Concept
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Smith, D. L., D. Neudecker, and R. Capote Noy. Investigation of the Effects of Probability Density Function Kurtosis on Evaluated Data Results. IAEA Nuclear Data Section, 2020. http://dx.doi.org/10.61092/iaea.3ar5-xmp8.

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In two previous investigations that are documented in this IAEA report series, we examined the effects of non-Gaussian, non-symmetric probability density functions (PDFs) on the outcomes of data evaluations. Most of this earlier work involved considering just two independent input data values and their respective uncertainties. They were used to generate one evaluated data point. The input data are referred to, respectively, as the mean value and standard deviation pair (y0,s0) for a prior PDF p0(y) and a second mean value and standard deviation pair (ye,se) for a likelihood PDF pe(y). Concept
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Hao, Wei-Da. Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6471.

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DESJARDIN, 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), 2002. http://dx.doi.org/10.2172/801388.

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Chow, Winston C. Analysis of the Probability Density Function of the Monopulse Ratio Radar Signal. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada315600.

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Ide, Kayo. Predictability and Ensemble Forecast Skill Enhancement Based on the Probability Density Function Estimation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada429618.

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Smith, Donald L., Denise Neudecker, and Roberto Capote Noy. Investigation of the Effects of Probability Density Function Kurtosis on Evaluated Data Results. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1434430.

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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada630373.

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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada624633.

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