VBEM_step

Variational Bayes Expectation Maximization

Our approach uses a  mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).

Kylliann De Santiago

mimiSBM

Mixture of Multilayer Integrator Stochastic Block Models

Marie Szafranski

Christophe Ambroise

VBEM_step function

<dl><dt>A</dt>
<dd>an array of dim=c(N,N,V)</dd>
<dt>params</dt>
<dd>list of parameters of the model</dd>
<dt>alternate</dt>
<dd>boolean indicated if we put an M-step after each part of the E-step, after u optimization and after tau optimization. If not, we optimize u and tau and after the M-step is made.</dd>
<dt>eps_conv</dt>
<dd>parameter of convergence for tau.</dd></dl>

Arguments

Variational Bayes Expectation Maximization — VBEM_step

<dl>

<dt>A</dt>
<dd>an array of dim=c(N,N,V)</dd>


<dt>params</dt>
<dd>list of parameters of the model</dd>


<dt>alternate</dt>
<dd>boolean indicated if we put an M-step after each part of the E-step, after u optimization and after tau optimization. If not, we optimize u and tau and after the M-step is made.</dd>


<dt>eps_conv</dt>
<dd>parameter of convergence for tau.</dd>

</dl>

VBEM_step: Variational Bayes Expectation Maximization

Description

Usage

Value

Arguments