plot_prior

Visualize the implied prior distribution for the partial correlations. This is
 particularly useful for the Bayesian hypothesis testing methods.

Fit Bayesian Gaussian graphical models. The methods are separated into
two Bayesian approaches for inference: hypothesis testing and estimation. There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical models,
and node wise predictability. These methods were recently introduced in the Gaussian
graphical model literature, including
Williams (2019) <doi:10.31234/osf.io/x8dpr>,
Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Philippe Rast

BGGM

Bayesian Gaussian Graphical Models

Donald Williams

Joris Mulder

plot_prior function

<dl><dt>prior_sd</dt>
<dd>Scale of the prior distribution, approximately the standard deviation
of a beta distribution (defaults to 0.5).</dd>
<dt>iter</dt>
<dd>Number of iterations (prior samples; defaults to 5000).</dd></dl>

Arguments

Plot: Prior Distribution — plot_prior

<dl>

<dt>prior_sd</dt>
<dd>Scale of the prior distribution, approximately the standard deviation
of a beta distribution (defaults to 0.5).</dd>


<dt>iter</dt>
<dd>Number of iterations (prior samples; defaults to 5000).</dd>

</dl>

plot_prior: Plot: Prior Distribution

Description

Usage

Value

Arguments

Examples