The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 20, No. 2 (Jun., 1992), pp. 171-185 (15 pages) A method for nonparametric estimation of density based on a randomly ...
Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is ...
Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...
This paper develops nonparametric deconvolution density estimation over SO(N), the group of N × N orthogonal matrices of determinant 1. The methodology is to use the group and manifold structures to ...
CATALOG DESCRIPTION: Fundamental and advanced topics in statistical pattern recognition including Bayesian decision theory, Maximum-likelihood and Bayesian estimation, Nonparametric density estimation ...
In this section we look at some numerical examples and discuss implementations of the nonparametric learning algorithms for density estimation we have discussed in this paper. As example, consider a ...
After publication of an earlier version of this paper, we received feedback that there were several incorrect references to related methods in the literature. These errors are corrected in the current ...
The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
CATALOG DESCRIPTION: Fundamental and advanced topics in statistical pattern recognition including Bayesian decision theory, Maximum-likelihood and Bayesian estimation, Nonparametric density estimation ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback