reattach_point

Attach a subtree to a given DDT at a randomly selected location

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

reattach_point function

<dl><dt>tree_kept</dt>
<dd>the tree to be attached to</dd>
<dt>c</dt>
<dd>hyparameter of divergence function a(t)</dd>
<dt>c_order</dt>
<dd>equals 1 (default) or 2 to choose divergence function
\(a(t) = c/(1-t) or c/(1-t)^2\).</dd>
<dt>alpha, theta</dt>
<dd>hyparameter of branching probability a(t) Gamma(m-alpha) / Gamma(m+1+theta)
For DDT, alpha = theta = 0. For general multifurcating tree from a Pitman-Yor process,
specify positive values to alpha and theta. It is, however, recommended using alpha =
theta = 0 in inference because multifurcating trees have not been tested rigorously.</dd></dl>

Arguments

Attach a subtree to a given DDT at a randomly selected location — reattach_point

<dl>

<dt>tree_kept</dt>
<dd>the tree to be attached to</dd>


<dt>c</dt>
<dd>hyparameter of divergence function a(t)</dd>


<dt>c_order</dt>
<dd>equals 1 (default) or 2 to choose divergence function
\(a(t) = c/(1-t) or c/(1-t)^2\).</dd>


<dt>alpha, theta</dt>
<dd>hyparameter of branching probability a(t) Gamma(m-alpha) / Gamma(m+1+theta)
For DDT, alpha = theta = 0. For general multifurcating tree from a Pitman-Yor process,
specify positive values to alpha and theta. It is, however, recommended using alpha =
theta = 0 in inference because multifurcating trees have not been tested rigorously.</dd>

</dl>

reattach_point: Attach a subtree to a given DDT at a randomly selected location

Description

Usage

Value

Arguments

See Also