dsdp: 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, 'dsdp' computes parameters of Gaussian
or exponential distributions together with degrees of polynomials by
a grid search, and coefficient of polynomials by a variant of semidefinite
programming. It adopts Akaike Information Criterion for model selection.
See a vignette for a tutorial and more on our 'Github' repository
<https://github.com/tsuchiya-lab/dsdp/>.
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