Results 231 to 240 of about 392,201 (264)

Adaptive density estimation

2006
This demonstration illustrates the APDF tree: an adaptive tree that supports the effective and effcient computation of continuous density information. The APDF tree allocates more partition points in non-linear areas of the density function and fewer points in linear areas of the density function.
Mazeika, Arturas, Böhlen, Michael H, Taliun, Andrej   +2 more
openaire   +2 more sources

Density estimation for dynamic volumes

Computers & Graphics, 2007
We propose a new approach to capture the volumetric density of dynamic scattering media instantaneously with a single image. The volume is probed with a set of laser lines and the scattered intensity is recorded by a conventional camera. We then determine the density along the laser lines taking the scattering properties of the media into account.
Christian Fuchs 0004, Tongbo Chen, Michael Goesele, Holger Theisel, Hans-Peter Seidel   +4 more
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SUBSAMPLING FOR DENSITY ESTIMATION

Statistics & Risk Modeling, 2002
Summary: We consider nonparametric density estimation from the point of view of coverage probability. To take into account the problem of bias in bootstrapping nonparametric density kernel estimators, \textit{P. Hall} [Statistics 22, No. 2, 215-232 (1991; Zbl 0809.62031); Ann. Stat. 20, No. 2, 675-694 (1992; Zbl 0748.62028)] showed that it is better to
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Estimation of Functionals of a Density

Theory of Probability & Its Applications, 1993
See the review in Zbl 0762.62012.
openaire   +1 more source

Direct Density Derivative Estimation

Neural Computation, 2016
Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative
Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu 0001, Masashi Sugiyama   +3 more
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Estimating the Variance of a Kernel Density Estimation

2010
This article proposes an interval-valued extension of kernel density estimation. We show that the imprecision of this interval-valued estimation is highly correlated with the variance of the density estimation induced by the statistical variations of the set of observations.
Bilal Nehme, Olivier Strauss, Kevin Loquin   +2 more
openaire   +1 more source

Autoregressive Conditional Density Estimation

International Economic Review, 1994
Summary: \textit{R. F. Engle's} ARCH model [Econometrica 50, 987-1007 (1982; Zbl 0491.62099)] is extended to permit parametric specifications for conditional dependence beyond the mean and variance. The suggestion is to model the conditional density with a small number of ``parameters'', and then model these parameters as functions of the conditioning ...
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Pareto Density Estimation: A Density Estimation for Knowledge Discovery

2005
Pareto Density Estimation (PDE) as defined in this work is a method for the estimation of probability density functions using hyperspheres. The radius of the hyperspheres is derived from optimizing information while minimizing set size. It is shown, that PDE is a very good estimate for data containing clusters of Gaussian structure. The behavior of the
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A Brief Survey of Bandwidth Selection for Density Estimation

Journal of the American Statistical Association, 1996
M C Jones, J S Marron
exaly   +2 more sources

TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Yinsong Wang, Yu Ding, Shahin Shahrampour   +2 more
exaly