dsdp: dsdp: Density Estimation using Semidefinite Programming
In dsdp: Density Estimation with Semidefinite Programming
dsdp R Documentation
dsdp: Density Estimation using Semidefinite Programming
Description
Density estimation with Semidefinite Programming.
The models of probability density functions are Gaussian or exponential
distributions with polynomial correction terms. Using a maximum likelihood
method, it computes parameters of Gaussian or exponential distributions
together with degrees of polynomials by a grid search, and coefficients
of polynomials by a variant of semidefinite programming.
It adopts Akaike Information Criterion for model selection.
See vignettes for tutorials and more information.
dsdp documentation built on Feb. 16, 2023, 8:36 p.m.
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| dsdp | R Documentation |
dsdp: Density Estimation using Semidefinite Programming
Description
Density estimation with Semidefinite Programming. The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, it computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficients of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See vignettes for tutorials and more information.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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