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Showing 1 to 20 of 316 for “"Density function"”.
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Estimation of a density function with applications to reliability
… of estimation of a univariate probability density function. Let Y₁, Y₂, …, Y<sub>n</sub> be a sample of n independent observations, each distributed according to an unknown continuous density function f(y). Given this sequence of observations, how can one estimate f(y)? Chapter I presents …
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Fast Clustering Using a Grid-Based Underlying Density Function Approximation
… is proposed to solve some of these issues. Density-based clustering algorithms use a concept called the “underlying density function”, which is a conceptual higher-dimension function that describes the possible results from the continuous data set that our input data is just a discrete …
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Probability Density Function Modeling of Turbulent Non-reactive and Reactive Spray Flows
Turbulente Sprays werden häufig im praktischen Verbrennungsystem angetroffen. Die Eigenschaften der turbulenten Sprays, wie Verteilung der Tröpfchengrößen und die Vermischung von Kraftstoff und Luft sind für die Effizienz, die Stabilität, und das Emissionsverhalten der Verbrennungprozesse sehr …
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Probability Density Function Estimation Applied to Minimum Bit Error Rate Adaptive Filtering
… 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 …
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The best truncation point for the estimated spectral density function of a stationary time series
… analysis, the scientist estimates the spectral density function of the process. One type of spectral density estimator is obtained by using the periodogram representation of the spectral density function. However, if the estimator is to be consistent, a weight function which satisfies certain …
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Uncertainties in Oceanic Microwave Remote Sensing: The Radar Footprint, the Wind-Backscatter Relationship, and the Measurement Probability Density Function
… and the Cramer-Rao lower bound. The probability density function of modified periodogram averages (a spectral estimation technique) is derived in generality and for the specific case of power estimates made by the NASA scatterometer. The impact on wind retrieval is quantified.</p>
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The Creation of Algorithms Designed for Analyzing Periodic Surfaces of Crystals and Mineralogically Important Sites in Molecular Models of Crystals: Understanding the Electron Density Function Through Visual Examinations of the Curvature and Shape of the Equi-Value Laplacian Surfaces
… surfaces of the Laplacian of the electron density function in selected molecules. The water molecule, H2O, was studied to demonstrate that these new methods are capable of reproducing known features. The disiloxane molecule, H6Si2O7, was studied because it serves as a model for bonding in …
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The Uniform Convergence of Special Standardized Distributions
… is standardized, and then the probability density function of the standardized Gamma distribution is shown to converge uniformly to the probability density function of the normal distribution. The F-distribution is standardized, and then the probability density function of the standardized …
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Analytical determination of autocorrelation and noise power density spectrum of randomly modulated pulse width square waves
… is well furnished by knowing the autocorrelation function of the input to the linear system. This input signal is generally an additive mixture of a piecewise continuous message and a noise. The problem considered in this paper is the determination of the autocorrelation function and also their …
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Logspline Density Estimation with an Application to the Study of Survival Data of Lung Cancer Patients.
<p>A Logspline method of estimating an unknown density function <em>f</em> based on sample data is studied. Our approach is to use maximum likelihood estimation to estimate the unknown density function from a space of linear splines that have a finite number of fixed uniform knots. In the end of …
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Spectral densities of discrete and continuous-indexed random fields
… conditions uniformly, each will have a spectral density function (not necessarily continuous) that is bounded between two positive constants. These spectral density functions will converge in a weak sense to another function (not necessarily continuous) that is also bounded between two positive …
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Light Scattering Measurement of Phospholipid Microvesicle Membrane Fusion Kinetics
The moments of the time constant density function, as measured by quasi-elastic light scattering and Koppel's method of cumulants, are expressed in terms of moments of the size density function of single-walled hollow spherical scatterers. An equation governing the evolution of fusing particles is …
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Bayesian Damage Identification from Elastostatic Data
… structure for modelling a suitable prior density function for the unknown parameters. Third, we assume a multi-variate Gaussian density function for the observation error and derive an approximate posterior density function for the unknowns from the prior density function and the forward …
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Reliability assessment under incomplete information: an evaluative study
… between the variables when the joint probability density function is unknown. There are no reports that provide information of the accuracy of these methods. This work presents an evaluative study of reliability under incomplete information, comparing three existing methods for calculating the …
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Bayes and minimum variance unbiased estimators of reliability using the truncated Weibull life testing model
… we shall estimate the corresponding reliability function of the above estimators. The first type of estimator of θ and the reliability function we will consider is the Bayes estimator using the general uniform, exponential, and inverted gamma distributions as prior probability density functions …
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Least squares mixture decomposition estimation
… Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized weight to each data point via …
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Distributed, adaptive deployment for nonholonomic mobile sensor networks : theory and experiments
… mobile sensors are able to estimate and map a density function in the sampling space without a previous knowledge of the environment. The controller is decentralized, which means that each mobile sensor has its own estimate and computes its own control input based on local information. In order …
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Density estimation and some topics in multivariate analysis
… statistic for the standardized bivariate normal density. Attempts to fit a four-parameter generalized gamma density function to this empirical distribution were only partially successful. Part II, entitled "The centroid method of numerical integration", begins with a discussion of the often …
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Capacitated, unbalanced p-median problems on a chain graph with a continuum of link demands
… is characterized by some weighted probability density function defined on the chain graph. The objective is to minimize the total (expected) transportation cost. This location-allocation problem is also referred to as the capacitated p-median problem on a chain graph. Two unbalanced cases of …
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Empirical Bayes procedures in time series analysis
… with auto-correlated errors and the spectral density function. In each case, empirical Bayes estimators are obtained using asymptotic or approximate distributions of the usual estimators. The Parzen, Tukey and Bartlett smoothing coefficients are all used in the estimation of the spectral …
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