lm.logpost.bas

This is a placeholder version of the function.
It falls back to <code>gaussian.loglik.g</code> and raises a warning if the full function is not loaded.

Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

Jon Lachmann

FBMS

Flexible Bayesian Model Selection and Model Averaging

Aliaksandr Hubin

lm.logpost.bas function

<dl><dt>y</dt>
<dd>A vector containing the dependent variable</dd>
<dt>x</dt>
<dd>The matrix containing the precalculated features</dd>
<dt>model</dt>
<dd>A logical vector indicating which features are included in the model</dd>
<dt>complex</dt>
<dd>A list of complexity measures for the features</dd>
<dt>mlpost_params</dt>
<dd>A list of parameters for the log likelihood, supplied by the user</dd></dl>

Arguments

Log likelihood function for Gaussian regression with parameter priors from BAS package — lm.logpost.bas

<dl>

<dt>y</dt>
<dd>A vector containing the dependent variable</dd>


<dt>x</dt>
<dd>The matrix containing the precalculated features</dd>


<dt>model</dt>
<dd>A logical vector indicating which features are included in the model</dd>


<dt>complex</dt>
<dd>A list of complexity measures for the features</dd>


<dt>mlpost_params</dt>
<dd>A list of parameters for the log likelihood, supplied by the user</dd>

</dl>

lm.logpost.bas: Log likelihood function for Gaussian regression with parameter priors from BAS package

Description

Usage

Value

Arguments

Examples