mix3gauss

Datasets generated by Mixture of 3 Gaussian Distributions whose density is
proportional to:
$$\exp(\frac{x^2}{2}) + 5\exp(\frac{(x-1)^2}{0.2}) +
3\exp(\frac{(x-1)^2}{0.5}).$$

datasets

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/>.

Satoshi Kakihara

dsdp

Density Estimation with Semidefinite Programming

Takashi Tsuchiya

mix3gauss function

A list of numeric vectors of Unimodal Gaussian Asymmetric Mixture
Model.<dl>
<dt>n200</dt>
<dd>A numeric vector with 200 elements.</dd><dt>n400</dt>
<dd>A numeric vector with 400 elements.</dd><dt>n600</dt>
<dd>A numeric vector with 600 elements.</dd><dt>n800</dt>
<dd>A numeric vector with 800 elements.</dd><dt>n1000</dt>
<dd>A numeric vector with 1000 elements.</dd><dt>n1200</dt>
<dd>A numeric vector with 1200 elements.</dd>
</dl>

Format

Datasets of Mixture of 3 Gaussian Distributions — mix3gauss

A list of numeric vectors of Unimodal Gaussian Asymmetric Mixture
Model.<dl>
<dt>n200</dt>
<dd>A numeric vector with 200 elements.</dd>

<dt>n400</dt>
<dd>A numeric vector with 400 elements.</dd>

<dt>n600</dt>
<dd>A numeric vector with 600 elements.</dd>

<dt>n800</dt>
<dd>A numeric vector with 800 elements.</dd>

<dt>n1000</dt>
<dd>A numeric vector with 1000 elements.</dd>

<dt>n1200</dt>
<dd>A numeric vector with 1200 elements.</dd>


</dl>

mix3gauss: Datasets of Mixture of 3 Gaussian Distributions

Description

Usage

Arguments

Format