cdf_gaussmodel

A cumulative distribution function(CDF) of Gaussian-based model.
To access parameters and coefficients in an object <code>gmodel</code>
of a class <code>gaussmodel</code>, use <code>gmodel$result[k, "mu1"]</code>,
<code>gmodel$result[k, "sig1"]</code>, <code>gmodel$coeffs[[k]]</code> for some index <code>k</code>.
This index appears in the leftmost column of estimation table generated by
<code>summary(gmodel)</code>.

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

cdf_gaussmodel function

<dl><dt>coeff</dt>
<dd>A coefficient vector in increasing order of degrees;
the first element is 0th degree, ..., and last element is the largest degree
of coefficients.</dd>
<dt>mu</dt>
<dd>A mean of Gaussian distribution.</dd>
<dt>sig</dt>
<dd>A standard deviation of Gaussian distribution, which is positive.</dd>
<dt>x</dt>
<dd>A numeric input vector.</dd></dl>

Arguments

Cumulative distribution function of Gaussian-based model — cdf_gaussmodel

<dl>

<dt>coeff</dt>
<dd>A coefficient vector in increasing order of degrees;
the first element is 0th degree, ..., and last element is the largest degree
of coefficients.</dd>


<dt>mu</dt>
<dd>A mean of Gaussian distribution.</dd>


<dt>sig</dt>
<dd>A standard deviation of Gaussian distribution, which is positive.</dd>


<dt>x</dt>
<dd>A numeric input vector.</dd>

</dl>

cdf_gaussmodel: Cumulative distribution function of Gaussian-based model

Description

Usage

Value

Arguments

See Also

Examples