number_clusters

Computes the estimated number of observational clusters (OC) and distributional clusters (DC) from a fitted SAN model object.
For variational inference (<code>SANvi</code> objects), the function returns point estimates based on posterior mode assignments.
For MCMC-based inference (<code>SANmcmc</code> objects), it returns the mean, median, and variance of the number of clusters across posterior samples.

An efficient tool for fitting nested mixture models based on a shared set of
atoms via Markov Chain Monte Carlo and variational inference algorithms.
Specifically, the package implements the common atoms model (Denti et al., 2023),
its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite
model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures
with Gaussian kernels equipped with a normal-inverse gamma prior distribution
on the parameters. Additional functions are provided to help analyze the
results of the fitting procedure.
References:
Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,
D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>,
D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

Francesco Denti

sanba

Fitting Shared Atoms Nested Models via MCMC or Variational Bayes

Laura D'Angelo

number_clusters function

<dl><dt>object</dt>
<dd>An object of class <code>SANvi</code> or <code>SANmcmc</code>, typically, the output of a call to <code>fit_fiSAN</code>, <code>fit_fSAN</code>, or <code>fit_CAM</code>.</dd>
<dt>...</dt>
<dd>ignored.</dd></dl>

Arguments

Estimate the Number of Observational and Distributional Clusters — number_clusters

<dl>

<dt>object</dt>
<dd>An object of class <code>SANvi</code> or <code>SANmcmc</code>, typically, the output of a call to <code>fit_fiSAN</code>, <code>fit_fSAN</code>, or <code>fit_CAM</code>.</dd>


<dt>...</dt>
<dd>ignored.</dd>

</dl>

number_clusters: Estimate the Number of Observational and Distributional Clusters

Description

Usage

Value

Arguments

Examples