Relevant bibliographies by topics / Probability Density Functionals

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probability Density Functionals'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Probability Density Functionals"

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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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Lapko, Aleksandr V., and Vasiliy A. Lapko. "Estimation of the integral of the square of derivatives of symmetric probability densities of one-dimensional random variables." Metrologiya, no. 1 (2020): 15–27. http://dx.doi.org/10.32446/0132-4713.2020-1-15-27.

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When substantiating the method of fast selection of the bandwidth of kernel probability density estimates, a constant was found that is a functional of the second density derivative. In this paper, the obtained result is generalized to derivatives of symmetric probability densities of different orders. The functional dependences of the constants under study on the coeffi cient of antikurtosis of a random variable are established. The regularities peculiar to them are investigated. Based on the results obtained, a method for estimating functionals from derived probability densities has been dev
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Gr�bel, Rudolf. "Estimation of density functionals." Annals of the Institute of Statistical Mathematics 46, no. 1 (1994): 67–75. http://dx.doi.org/10.1007/bf00773593.

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Alonso, Andrés M., and Antonio Cuevas. "On smoothed bootstrap for density functionals." Journal of Nonparametric Statistics 15, no. 4-5 (2003): 467–77. http://dx.doi.org/10.1080/10485250310001604613.

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Borovikov, V. P. "Estimation of Functionals of a Density." Theory of Probability & Its Applications 37, no. 3 (1993): 507–14. http://dx.doi.org/10.1137/1137100.

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Nadaraya, Elizbar, and Grigol Sokhadze. "On integral functionals of a density." Communications in Statistics - Theory and Methods 45, no. 23 (2016): 7086–102. http://dx.doi.org/10.1080/03610926.2014.974825.

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Lapko, Aleksandr V., and Vasiliy A. Lapko. "Estimation of a nonlinear functional from the probability density when optimizing nonparametric decision functions." Izmeritel`naya Tekhnika, no. 1 (2021): 14–20. http://dx.doi.org/10.32446/0368-1025it.2021-1-14-20.

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A method for estimating the nonlinear functional of the probability density of a two-dimensional random variable is proposed. It is relevant when implementing procedures for fast bandwidths selection in the problem of optimization of kernel probability density estimates. The solution of this problem allows to significantly improve the computational efficiency of nonparametric decision rules. The basis of the proposed approach is the analysis of the formula for the optimal bandwidth of the kernel probability density estimation. In this case, the bandwidth of kernel functions is represented as t
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Martinez, Harú V., and Marı́a M. Olivares. "Estimation of quadratic functionals of a density." Statistics & Probability Letters 42, no. 4 (1999): 327–32. http://dx.doi.org/10.1016/s0167-7152(98)00193-x.

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BURKE, KIERON, and JOHN P. PERDEW. "DENSITY FUNCTIONALS AND SMALL INTERPARTICLE SEPARATIONS IN ELECTRONIC SYSTEMS." Modern Physics Letters B 09, no. 14 (1995): 829–38. http://dx.doi.org/10.1142/s0217984995000784.

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We review some recent results concerning the probability that two electrons will be found close together in any interacting electronic system, and why this probability is usually well approximated by local (LSD) and semilocal spin density functional theories. The success of these approximations for the energy in "normal" systems is explained by the usual sum rule arguments on the system- and spherically-averaged exchange-correlation hole density <n xc (u)>, coupled with the nearly correct, but not exact, behavior of these approximations as the interelectronic separation u → 0. We argue t
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Donoho, David L. "One-Sided Inference about Functionals of a Density." Annals of Statistics 16, no. 4 (1988): 1390–420. http://dx.doi.org/10.1214/aos/1176351045.

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Dissertations / Theses on the topic "Probability Density Functionals"

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Santos, 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.

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Master of Science in Finance<br>This 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 metho
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Becvar, Martin. "Estimating typical sediment concentration probability density functions for European rivers." Thesis, Cranfield University, 2005. http://hdl.handle.net/1826/1016.

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Sediment in rivers is linked with qualitative and quantitative water problems throughout Europe. Sediment supply and transfer are part of a natural process of minimising gradients in the landscape. However, since human activities have started to affect the equilibrium, sediment supply is often out of balance with the river system. Cases of either low or high concentration often mean an instability which may cause severe problems. Therefore it is highly important to gain knowledge about sediment patterns in catchments as a part of catchment management. This study was undertaken in order to imp
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Heinemann, 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.

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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.

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Les crues et les sécheresses sont deux des risques majeurs en France et nécessitent une attention particulière. Dans ces conditions où le changement climatique engendre des phénomènes extrêmes de plus en plus fréquents, la modélisation de ces risques est désormais un élément incontournable pour la gestion de la ressource en eau.Actuellement, les débits ou hauteurs d’eau sont principalement anticipés à partir de modèles à base physique ou conceptuelle. Bien qu’efficaces et nécessaires, la calibration et la mise en œuvre de ces modèles nécessitent la réalisation d’études longues et coûteuses.Dan
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Cabral, Victor Christopher. "Using simulation to estimate probability density functions of bound water molecules with DSI." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41684.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2007.<br>"June 2007." Leaves unnumbered.<br>Includes bibliographical references (leaves [58]-[59]).<br>In order to explore the physical basis by which high resolution diffusion imaging derives information about fiber alignment and diameter, we simulated a model diffusion experiment employing a random walk paradigm. A simulation of a model diffusion spectrum imaging experiment was written in Java in order to compare the diffusive behavior of particles in a perfectly reflecting channel with the intern
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Kharoufeh, Jeffrey P. "Density estimation for functions of correlated random variables." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177097417.

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Ferchichi, Mohsen. "Measurements of small-scale statistics and probability density functions in passively heated shear flow." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0019/NQ48098.pdf.

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Forrester, David Edward Economics Australian School of Business UNSW. "Market probability density functions and investor risk aversion for the australia-us dollar exchange rate." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/27199.

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This thesis models the Australian-US Dollar (AUD/USD) exchange rate with particular attention being paid to investor risk aversion. Accounting for investor risk aversion in AUD/USD exchange rate modelling is novel, so too is the method used to measure risk aversion in this thesis. Investor risk aversion is measured using a technique developed in Bliss and Panigirtzoglou (2004), which makes use of Probability Density Functions (PDFs) extracted from option markets. More conventional approaches use forward-market pricing or Uncovered Interest Parity. Several methods of estimating PDFs from option
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Pokhrel, Keshav Prasad. "Statistical Analysis and Modeling of Brain Tumor Data: Histology and Regional Effects." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4746.

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Comprehensive statistical models for non-normally distributed cancerous tumor sizes are of prime importance in epidemiological studies, whereas a long term forecasting models can facilitate in reducing complications and uncertainties of medical progress. The statistical forecasting models are critical for a better understanding of the disease and supply appropriate treatments. In addition such a model can be used for the allocations of budgets, planning, control and evaluations of ongoing efforts of prevention and early detection of the diseases. In the present study, we investigate the effect
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Flores, Philippe. "Estimation of high dimensional probability density functions with low rank-tensors models : application to flow cytometry." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0021.

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La cytométrie en flux (CMF) est une technique d'analyse de cellules biologiques largement utilisée en immunologie, par exemple dans la recherche sur les leucémies. Le principe de la CMF est de mesurer les propriétés de fluorescence individuellement dans un volume de cellules. L'analyse des données de CMF permet d'identifier et de caractériser les populations de cellules à l'intérieur d'un volume de cellules. Les analyses effectuées manuellement reposent sur la sélection de cellules sur des nuages de points bivariés. Cette opération, appelée gating, prend du temps et est subjective. Bien qu'il
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Books on the topic "Probability Density Functionals"

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Probability density functions in turbulent channel flow. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Probability density functions in turbulent channel flow. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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Ronald, Johnson L., Smith P. L, and United States. National Aeronautics and Space Administration., eds. Probability density functions of observed rainfall in Montana. National Aeronautics and Space Administration, 1995.

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Ronald, Johnson L., Smith P. L, and United States. National Aeronautics and Space Administration., eds. Probability density functions of observed rainfall in Montana. National Aeronautics and Space Administration, 1995.

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Canada, Atomic Energy of. Guidelines for defining probability density functions for SYVAC3-CC3 parameters. Atomic Energy of Canada Limited, 1989.

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Moek, G. Verification of obstacle accountability areas using a simple mathematical model. Part I: Description of general model and application for a specific case. National Aerospace Laboratory, 1985.

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Bahra, Bhupinder. Implied risk-neutral probability density functions from option prices: Theory and application. Bank of England, 1997.

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Bahra, Bhupinder. Implied risk-neutral probability density functions from option prices: Theory and application. Bank of England, 1997.

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Morrison, Desmond Kenneth. Display of probability density functions using the PC and MC68HC711 micro-controller. The Author], 1994.

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Brenner, Marty. Nonstationary dynamics data analysis with wavelet-SVD filtering. National Aeronautics and Space Administration, Dryden Flight Research Center, 2001.

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Book chapters on the topic "Probability Density Functionals"

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Castaing, B. "Probability Density Functions." In Turbulence. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2586-8_13.

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Lista, Luca. "Probability Density Functions." In Statistical Methods for Data Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19934-9_3.

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Kay, Steven M. "Conditional Probability Density Functions." In Intuitive Probability and Random Processes Using MATLAB®. Springer US, 2012. http://dx.doi.org/10.1007/0-387-24158-2_13.

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Putcha, Chandrasekhar, Subhrajit Dutta, and Sanjay K. Gupta. "Probability and Density Functions." In Reliability and Risk Analysis in Engineering and Medicine. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80454-1_1.

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Bhat, Sooraj, Johannes Borgström, Andrew D. Gordon, and Claudio Russo. "Deriving Probability Density Functions from Probabilistic Functional Programs." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36742-7_35.

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Diwekar, Urmila, and Amy David. "Probability Density Functions and Kernel Density Estimation." In SpringerBriefs in Optimization. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2282-6_3.

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Tveito, Aslak, and Glenn T. Lines. "Properties of Probability Density Functions." In Lecture Notes in Computational Science and Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30030-6_4.

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Tveito, Aslak, and Glenn T. Lines. "Generalized Systems Governing Probability Density Functions." In Lecture Notes in Computational Science and Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30030-6_7.

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McDaniel, Lee S., Andrew G. Glen, and Lawrence M. Leemis. "Linear Approximations of Probability Density Functions." In International Series in Operations Research & Management Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43317-2_10.

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Rose, Colin, and Murray D. Smith. "Random[Title]: Manipulating Probability Density Functions." In Computational Economics and Finance. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2340-5_16.

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Conference papers on the topic "Probability Density Functionals"

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Ajgl, Jiří, and Ondřej Straka. "On fusion of probability density functions using tensor train decomposition." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706475.

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Ye, Yogendra P. "Nonparametric Smooth Estimation of Probability Density Function and Other Related Functionals: Some New Developments." In International Conference on Statistics: Theory and Applications (ICSTA'19). Avestia Publishing, 2019. http://dx.doi.org/10.11159/icsta19.01.

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Mohammed Ismail, Wazim, and Chung-chieh Shan. "Deriving a probability density calculator (functional pearl)." In ICFP'16: ACM SIGPLAN International Conference on Functional Programming. ACM, 2016. http://dx.doi.org/10.1145/2951913.2951922.

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Bhat, Sooraj, Ashish Agarwal, Richard Vuduc, and Alexander Gray. "A type theory for probability density functions." In the 39th annual ACM SIGPLAN-SIGACT symposium. ACM Press, 2012. http://dx.doi.org/10.1145/2103656.2103721.

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Rossouw, J. "Design Waves and Their Probability Density Functions." In 21st International Conference on Coastal Engineering. American Society of Civil Engineers, 1989. http://dx.doi.org/10.1061/9780872626874.062.

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Stow, C. M. "Experimental issues of functional merging on probability density estimation." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970713.

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KIM, YONGMO, and T. CHUNG. "Turbulent combustion analysis with various probability density functions." In 9th Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1991.

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Aime, Claude. "Probability density functions for speckle imaging in astronomy." In International Conference on Holography and Correlation Optics, edited by Oleg V. Angelsky. SPIE, 1995. http://dx.doi.org/10.1117/12.226714.

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Crespo, L. G., and J. Q. Sun. "Nonlinear stochastic control via stationary probability density functions." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1023933.

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Adurthi, Nagavenkat, Puneet Singla, and Tarunraj Singh. "Conjugate unscented transform rules for uniform probability density functions." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580202.

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Reports on the topic "Probability Density Functionals"

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Kitsul, Yuriy, and Jonathan Wright. The Economics of Options-Implied Inflation Probability Density Functions. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18195.

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Kamrath, Matthew, D. Wilson, Carl Hart, Daniel Breton, and Caitlin Haedrich. Evaluating parametric probability density functions for urban acoustic noise. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/38006.

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Poppeliers, Christian, and Leiph Preston. An efficient method to estimate the probability density of seismic Green's functions. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1813651.

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Maddalena, R. L., T. E. McKone, A. Bodnar, and J. Jacobson. Development and evaluation of probability density functions for a set of human exposure factors. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/752240.

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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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Givi, P., and P. A. McMurtry. Direct Numerical Simulations of the PDF's (Probability Density Functions) of a Passive Scalar in a Forced Mixing Layer,. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada192134.

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Wilson, D., Vladimir Ostashev, and Chris Pettit. Distribution of the two-point product of complex amplitudes in the fully saturated scattering regime. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/38701.

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This Letter considers probability density functions (pdfs) involving products of the complex amplitudes observed at two points (which may, in general, involve separations in space, time, or frequency) in conditions of fully saturated scattering. First, the pdf is derived for the product of the complex amplitude at one point with the conjugate of the complex amplitude at another point. It is shown that the real and imaginary parts of this product each have a variance gamma pdf. Second, expressions are derived for several joint pdfs involving complex amplitude products and powers at two points.
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Gaglianone, Wagner Piazza, and Waldyr Dutra Areosa. Financial Conditions Indicator for Brazil. Inter-American Development Bank, 2017. http://dx.doi.org/10.18235/0011805.

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This paper proposes a methodology for constructing a Financial Conditions Indicator (FCI) based on factor analysis and the approaches of Brave and Butters (2011) and Aramonte et al. (2013). A selected set of variables is used and their information content aggregated into a single index that summarizes the overall financial conditions of the economy. The approach is further employed to forecast economic activity. An empirical exercise for Brazil is provided to illustrate the methodology, in which a reduced-form equation is employed to point forecast the growth rate of the Brazilian economy. In
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