cICS

C++ function to estimate Pitman-Yor univariate mixtures via importance conditional sampler - LOCATION SCALE

internal

Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.

Riccardo Corradin

BNPmix

Bayesian Nonparametric Mixture Models

cICS function

<dl><dt>data</dt>
<dd>a vector of observations</dd>
<dt>grid</dt>
<dd>vector to evaluate the density</dd>
<dt>niter</dt>
<dd>number of iterations</dd>
<dt>nburn</dt>
<dd>number of burn-in iterations</dd>
<dt>m0</dt>
<dd>expectation of location component</dd>
<dt>k0</dt>
<dd>tuning parameter of variance of location component</dd>
<dt>a0</dt>
<dd>parameter of scale component</dd>
<dt>b0</dt>
<dd>parameter of scale component</dd>
<dt>m1</dt>
<dd>mean of hyperdistribution of m0</dd>
<dt>s21</dt>
<dd>variance of hyperdistribution of m0</dd>
<dt>tau1</dt>
<dd>shape parameter of hyperdistribution of k0</dd>
<dt>tau2</dt>
<dd>rate parameter of hyperdistribution of k0</dd>
<dt>a1</dt>
<dd>shape parameter of hyperdistribution of b0</dd>
<dt>b1</dt>
<dd>rate parameter of hyperdistribution of b0</dd>
<dt>strength</dt>
<dd>parameter</dd>
<dt>napprox</dt>
<dd>number of approximating values</dd>
<dt>nupd</dt>
<dd>number of iterations to show current updating</dd>
<dt>out_param</dt>
<dd>if TRUE, return also the location and scale paramteres lists</dd>
<dt>out_dens</dt>
<dd>if TRUE, return also the estimated density (default TRUE)</dd>
<dt>discount</dt>
<dd>discount parameter</dd>
<dt>print_message</dt>
<dd>print the status</dd>
<dt>hyper, </dt>
<dd>if TRUE use hyperpriors, default TRUE</dd></dl>

Arguments

C++ function to estimate Pitman-Yor univariate mixtures via importance conditional sampler - LOCATION SCALE — cICS

<dl>

<dt>data</dt>
<dd>a vector of observations</dd>


<dt>grid</dt>
<dd>vector to evaluate the density</dd>


<dt>niter</dt>
<dd>number of iterations</dd>


<dt>nburn</dt>
<dd>number of burn-in iterations</dd>


<dt>m0</dt>
<dd>expectation of location component</dd>


<dt>k0</dt>
<dd>tuning parameter of variance of location component</dd>


<dt>a0</dt>
<dd>parameter of scale component</dd>


<dt>b0</dt>
<dd>parameter of scale component</dd>


<dt>m1</dt>
<dd>mean of hyperdistribution of m0</dd>


<dt>s21</dt>
<dd>variance of hyperdistribution of m0</dd>


<dt>tau1</dt>
<dd>shape parameter of hyperdistribution of k0</dd>


<dt>tau2</dt>
<dd>rate parameter of hyperdistribution of k0</dd>


<dt>a1</dt>
<dd>shape parameter of hyperdistribution of b0</dd>


<dt>b1</dt>
<dd>rate parameter of hyperdistribution of b0</dd>


<dt>strength</dt>
<dd>parameter</dd>


<dt>napprox</dt>
<dd>number of approximating values</dd>


<dt>nupd</dt>
<dd>number of iterations to show current updating</dd>


<dt>out_param</dt>
<dd>if TRUE, return also the location and scale paramteres lists</dd>


<dt>out_dens</dt>
<dd>if TRUE, return also the estimated density (default TRUE)</dd>


<dt>discount</dt>
<dd>discount parameter</dd>


<dt>print_message</dt>
<dd>print the status</dd>


<dt>hyper, </dt>
<dd>if TRUE use hyperpriors, default TRUE</dd>

</dl>

cICS: C++ function to estimate Pitman-Yor univariate mixtures via importance conditional sampler - LOCATION SCALE

Description

Arguments