plot_prior_number_of_components

This plots the prior distribution on the number of components for the stable
process. The Dirichlet process is provided for comparison.

Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.

Guillaume Kon Kam King

BNPdensity

Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures

Julyan Arbel

Ernesto Barrios

Antonio Lijoi

Luis E. Nieto-Barajas

Igor Prünster

plot_prior_number_of_components function

<dl><dt>n</dt>
<dd>Number of data points</dd>
<dt>Gama</dt>
<dd>Numeric constant. 0 &lt;= Gama &lt;=1.</dd>
<dt>Alpha</dt>
<dd>Numeric constant. Total mass of the centering measure for the
Dirichlet process.</dd>
<dt>grid</dt>
<dd>Integer vector. Level of truncation when computing the expectation. Defaults to
n. If greater than n, it is fixed to n.</dd>
<dt>silence</dt>
<dd>Boolean. Whether to print the current calculation step for the Stable process, as the function can be long</dd></dl>

Arguments

This plots the prior distribution on the number of components for the stable
process. The Dirichlet process is provided for comparison. — plot_prior_number_of_components

<dl>

<dt>n</dt>
<dd>Number of data points</dd>


<dt>Gama</dt>
<dd>Numeric constant. 0 &lt;= Gama &lt;=1.</dd>


<dt>Alpha</dt>
<dd>Numeric constant. Total mass of the centering measure for the
Dirichlet process.</dd>


<dt>grid</dt>
<dd>Integer vector. Level of truncation when computing the expectation. Defaults to
n. If greater than n, it is fixed to n.</dd>


<dt>silence</dt>
<dd>Boolean. Whether to print the current calculation step for the Stable process, as the function can be long</dd>

</dl>

plot_prior_number_of_components: This plots the prior distribution on the number of components for the stable process. The Dirichlet process is provided for comparison.

Description

Usage

Value

Arguments

Examples