rpmd

Generates random samples from the PMD specified by the success probability matrix.

Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.

Yili Hong

PoissonMultinomial

The Poisson-Multinomial Distribution

Yili Hong 

Zhengzhi Lin 

Yueyao Wang 

Florian Junge 

rpmd function

<dl><dt>pmat</dt>
<dd>An \(\rm n \times m\) success probability matrix, where \(\rm n\) is the number of independent trials and \(\rm m\) is the number of categories.
Each row of pmat contains the success probabilities for the corresponding
trial, and each row adds up to 1.</dd>
<dt>s</dt>
<dd>The number of samples to be generated.</dd></dl>

Arguments

Poisson-Multinomial Distribution Random Number Generator — rpmd

<dl>

<dt>pmat</dt>
<dd>An \(\rm n \times m\) success probability matrix, where \(\rm n\) is the number of independent trials and \(\rm m\) is the number of categories.
Each row of pmat contains the success probabilities for the corresponding
trial, and each row adds up to 1.</dd>


<dt>s</dt>
<dd>The number of samples to be generated.</dd>

</dl>

rpmd: Poisson-Multinomial Distribution Random Number Generator

Description

Usage

Value

Arguments

Examples