draw_mnorm

Efficiently sample multivariate normal using precision matrix
from \(x ~ N(Q^{-1}a, Q^{-1})\), where \(Q^{-1}\) is the precision matrix

Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis.
Reference: Li et al. (2023) <arXiv:2306.04700>.

Mengbing Li

ddtlcm

Latent Class Analysis with Dirichlet Diffusion Tree Process
Prior

Briana Stephenson

Zhenke Wu

draw_mnorm function

<dl><dt>precision_mat</dt>
<dd>precision matrix Q of the multivariate normal distribution</dd>
<dt>precision_a_vec</dt>
<dd>a vector a such that the mean of the multivariate normal distribution is
\(Q^{-1}a\)</dd></dl>

Arguments

Efficiently sample multivariate normal using precision matrix
from \(x ~ N(Q^{-1}a, Q^{-1})\), where \(Q^{-1}\) is the precision matrix — draw_mnorm

<dl>

<dt>precision_mat</dt>
<dd>precision matrix Q of the multivariate normal distribution</dd>


<dt>precision_a_vec</dt>
<dd>a vector a such that the mean of the multivariate normal distribution is
\(Q^{-1}a\)</dd>

</dl>

draw_mnorm: Efficiently sample multivariate normal using precision matrix from \(x ~ N(Q^{-1}a, Q^{-1})\), where \(Q^{-1}\) is the precision matrix

Description

Usage

Arguments