Готові списки джерел за темами / Estimation of probability density function

Добірка наукової літератури з теми "Estimation of probability density function"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Estimation of probability density function".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

BALÁZS, 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.

Повний текст джерела

Анотація:

Signal processing techniques will lean on blind methods in the near future, where no redundant, resource allocating information will be transmitted through the channel. To achieve a proper decision, however, it is essential to know at least the probability density function (PDF), which to estimate is classically a time consumpting and/or less accurate hard task that may make decisions to fail. This paper describes the design of a quantum assisted PDF estimation method also by way of an example, which promises to achieve the exact PDF by proper setting of parameters in a very rapid way.

Стилі APA, Harvard, Vancouver, ISO та ін.

Chen, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

López-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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Alencar, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Srikanth, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Kwasniok, 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.

Повний текст джерела

Анотація:

A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global maximum likelihood without any roughness penalty. A statistically orthogonal formulation of the inference problem and a numerically stable and fast convex optimization algorithm for its solution are presented. Automatic model selection over the type and number of basis functions is performed with the Bayesian information criterion. The methodology can naturally be applied to densities supported on bounded, infinite or semi-infinite domains without boundary bias. Relationships to the truncated moment problem and the moment-constrained maximum entropy principle are discussed and a new theorem on the existence of solutions is contributed. The new technique compares very favourably to kernel density estimation, the diffusion estimator, finite mixture models and local likelihood density estimation across a diverse range of simulation and observation data sets. The semiparametric estimator combines a very small mean integrated squared error with a high degree of smoothness which allows for a robust and reliable detection of the modality of the probability density in terms of the number of modes and bumps.

Стилі APA, Harvard, Vancouver, ISO та ін.

Xiang, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Kraft, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Oryshchenko, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Дисертації з теми "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.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Kharoufeh, Jeffrey P. "Density estimation for functions of correlated random variables." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177097417.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

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/.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Phillips, Kimberly Ann. "Probability Density Function Estimation Applied to Minimum Bit Error Rate Adaptive Filtering." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/33280.

Повний текст джерела

Анотація:

It is known that a matched filter is optimal for a signal corrupted by Gaussian noise. In a wireless environment, the received signal may be corrupted by Gaussian noise and a variety of other channel disturbances: cochannel interference, multiple access interference, large and small-scale fading, etc. Adaptive filtering is the usual approach to mitigating this channel distortion. Existing adaptive filtering techniques usually attempt to minimize the mean square error (MSE) of some aspect of the received signal, with respect to the desired aspect of that signal. Adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER). The main focus of this research involves estimation of the probability density function (PDF) of the received signal; this PDF estimate is used to adaptively determine a solution that minimizes BER. To this end, a new adaptive procedure called the Minimum BER Estimation (MBE) algorithm has been developed. MBE shows improvement over the Least Mean Squares (LMS) algorithm for most simulations involving interference and in some multipath situations. Furthermore, the new algorithm is more robust than LMS to changes in algorithm parameters such as stepsize and window width.
Master of Science

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Анотація:

Master of Science in Finance
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 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).

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Анотація:

[EN] Mathematical models based on deterministic differential equations do not take into account the inherent uncertainty of the physical phenomenon (in a wide sense) under study. In addition, inaccuracies in the collected data often arise due to errors in the measurements. It thus becomes necessary to treat the input parameters of the model as random quantities, in the form of random variables or stochastic processes. This gives rise to the study of random ordinary and partial differential equations. The computation of the probability density function of the stochastic solution is important for uncertainty quantification of the model output. Although such computation is a difficult objective in general, certain stochastic expansions for the model coefficients allow faithful representations for the stochastic solution, which permits approximating its density function. In this regard, Karhunen-Loève and generalized polynomial chaos expansions become powerful tools for the density approximation. Also, methods based on discretizations from finite difference numerical schemes permit approximating the stochastic solution, therefore its probability density function. The main part of this dissertation aims at approximating the probability density function of important mathematical models with uncertainties in their formulation. Specifically, in this thesis we study, in the stochastic sense, the following models that arise in different scientific areas: in Physics, the model for the damped pendulum; in Biology and Epidemiology, the models for logistic growth and Bertalanffy, as well as epidemiological models; and in Thermodynamics, the heat partial differential equation. We rely on Karhunen-Loève and generalized polynomial chaos expansions and on finite difference schemes for the density approximation of the solution. These techniques are only applicable when we have a forward model in which the input parameters have certain probability distributions already set. When the model coefficients are estimated from collected data, we have an inverse problem. The Bayesian inference approach allows estimating the probability distribution of the model parameters from their prior probability distribution and the likelihood of the data. Uncertainty quantification for the model output is then carried out using the posterior predictive distribution. In this regard, the last part of the thesis shows the estimation of the distributions of the model parameters from experimental data on bacteria growth. To do so, a hybrid method that combines Bayesian parameter estimation and generalized polynomial chaos expansions is used.
[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

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Книги з теми "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.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Devroye, Luc. Nonparametric density estimation: The L1 view. New York: Wiley, 1985.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

László, Györfi, ed. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Churnside, 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.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

R, Thompson James. Nonparametric function estimation, modeling, and simulation. Philadelphia: Society for Industrial and Applied Mathematics, 1990.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Yamazaki, 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.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Simon, 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.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Частини книг з теми "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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

López-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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

López-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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Gentle, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Murthy, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Zarch, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Mimura, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

López-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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Gentle, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Nadaraya, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Suga, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Mukherjee, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Jing 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Xie, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Mukherjee, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Dumitru, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Ide, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Ide, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Smith, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Clark, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Jordan, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Chow, 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

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

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!