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Relevant bibliographies by topics / Probability density function

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Academic literature on the topic 'Probability density function'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Probability density function"

1

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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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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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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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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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 generalized error function, the Zipf-Mandelbrot pdf, the generalized Gaussian and Laplace pdf. Their cumulative functions and moments were also obtained analytically.

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Ateia, Khalid A., and Tarig A. Abdelhaleem. "Appropriate Probability Density Function of Convex Bodies." Journal of The Faculty of Science and Technology, no. 6 (January 13, 2021): 130–39. http://dx.doi.org/10.52981/jfst.vi6.619.

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We investigate under the notion of Large Deviation Principle & Concentration of Measure as a technique,the ability of estimating the probability density function of any random vector in the space Rn. We found that an appropriate probability distribution for any convex body in the space is sub – Gaussian.

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Derksen, R. W., P. J. Sullivan, and H. Yip. "Asymptotic probability density function of a scalar." AIAA Journal 32, no. 5 (1994): 1083–84. http://dx.doi.org/10.2514/3.12099.

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Dissertations / Theses on the topic "Probability density function"

1

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 further develop a recently proposed non-parametric PDF estimation method for signals, called the NP windows method. We propose three new algorithms to compute PDF estimates using the NP windows method. One of these algorithms, called the log-basis algorithm, provides an easier and faster way to compute the NP windows estimate, and allows us to compare the NP windows method with the two existing popular estimators. Results show that the NP windows method is fast and can estimate PDFs with a significantly smaller amount of training data. Moreover, it does not require any additional parameter settings. To demonstrate utility of the NP windows method in image analysis we consider its application to image segmentation. To do this, we first describe the distribution of intensity values in the image with a mixture of non-parametric distributions. We estimate these distributions using the NP windows method. We then use this novel mixture model to evolve curves with the well-known level set framework for image segmentation. We also take into account the partial volume effect that assumes importance in medical image analysis methods. In the final part of the thesis, we apply our non-parametric mixture model (NPMM) based level set segmentation framework to segment colorectal MR images. The segmentation of colorectal MR images is made challenging due to sparsity and ambiguity of features, presence of various artifacts, and complex anatomy of the region. We propose to use the monogenic signal (local energy, phase, and orientation) to overcome the first difficulty, and the NPMM to overcome the remaining two. Results are improved substantially on those that have been reported previously. We also present various ways to visualise clinically useful information obtained with our segmentations in a 3-dimensional manner.

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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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6

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 parameters by the direct methodology are compared to the results of estimating the parameters by the selected reference methodologies for understanding if the direct methodology can be used for understanding the key parameters of the RND. The study is based on S&P 500 index option data from year 2008 for estimating the reference RNDs and for defining the reference moments from the reference RNDs. The S&P 500 futures contract data is necessary for finding the expectation value estimation for the direct methodology. Only few ATM and/or close ATM options from the S&P 500 index option data are necessary for getting the standard deviation estimation for the direct methodology. Both parametric and non-parametric methods were implemented for defining reference RNDs. The reference RND estimation results are presented so that the reference RND estimation methodologies can be compared to each other. The moments of the reference RNDs were calculated from the RND estimation results so that the moments of the direct methodology can be compared to the moments of the reference methodologies. The futures contracts are used in the direct methodology for getting the expectation value estimation of the RND. Only few ATM and/or close ATM options are used in the direct methodology for getting the standard deviation estimation of the RND. The implied volatility is calculated from option prices using ATM and/or close ATM options only. Based on implied volatility the standard deviation can be calculated directly using time scaling equations. Skewness and kurtosis can be calculated from the estimated expectation value and the estimated standard deviation by using the assumption of the lognormal distribution. Based on the results the direct methodology is acceptable for getting the expectation value estimation using the futures contract value directly instead of the expectation value, which is calculated from the RND of full option data, if and only if the time to maturity is relative short. The standard deviation estimation can be calculated from few ATM and/or at close ATM options instead of calculating the RND from full option data only if the time to maturity is relative short. Skewness and kurtosis were calculated from the expectation value estimation and the standard deviation estimation by using the assumption of the lognormal distribution. Skewness and kurtosis could not be estimated by using the assumption of the lognormal distribution because the lognormal distribution is not correct generic assumption for the RND distributions.

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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 model if, for the set of events they try to predict, the predicted frequencies derived from the model match the empirical ones derived from the data set. A model selection algorithm, based on the validation test, is developed which prevents both problems of over-fitting and under-fitting. We investigate the influence of the various parameters in the model selection method in order to develop it into a robust operational tool. The capability of the proposed method in real world applications is examined on the problem of face image segmentation for automatic initialisation of lip tracking systems. A segmentation approach is proposed which is based on Gaussian mixture modelling of the pixels RGB values using the predictive validation technique. The lip region segmentation is based on the estimated model. First a grouping of the model components is performed using a novel approach. The resulting groups are then the basis of a Bayesian decision making system which labels the pixels in the mouth area as lip or non-lip. The experimental results demonstrate the superiority of the method over the conventional clustering approaches. In order to improve the method computationally an image sampling technique is applied which is based on Sobol sequences. Also, the image modelling process is strengthened by incorporating spatial contextual information using two different methods, a Neigh-bourhood Expectation Maximisation technique and a spatial clustering method based on a Gibbs/Markov random field modelling approach. Both methods are developed within the proposed modelling framework. The results obtained on the lip segmentation application suggest that spatial context is beneficial.

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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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10

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"

1

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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2

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

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3

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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4

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

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5

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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6

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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8

Number theoretic density and logical limit laws. American Mathematical Society, 2001.

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9

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

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10

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

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Book chapters on the topic "Probability density function"

1

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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3

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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5

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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6

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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7

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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Gupta, Arjun K., Tamas Varga, and Taras Bodnar. "Probability Density Function and Expected Values." In Elliptically Contoured Models in Statistics and Portfolio Theory. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8154-6_3.

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Kassam, Saleem A. "Some Univariate Noise Probability Density Function Models." In Springer Texts in Electrical Engineering. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3834-8_3.

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Conference papers on the topic "Probability density function"

1

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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2

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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3

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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Su, Yu-Shih, Yi-Hsin Weng, and Shih-Chieh Chang. "Efficient Calculation of Timed Cumulative Probability Density Function." In 2007 International Symposium on VLSI Design, Automation and Test. IEEE, 2007. http://dx.doi.org/10.1109/vdat.2007.373252.

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Reports on the topic "Probability density function"

1

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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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada625720.

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Kolla, Hemanth, Saibal De, Reese Jones, Michael Hansen, and Julia Plews. Comprehensive uncertainty quantification (UQ) for full engineering models by solving probability density function (PDF) equation. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890060.

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Nuttall, Albert H. Saddlepoint Approximations for the Combined Probability and Joint Probability Density Function of Selected Order Statistics and the Sum of the Remainder. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421711.

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