Product function used in the deep regression context

models

methods

Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) <doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A., Storvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>, and Hubin, A., Heinze, G., & De Bin, R. (2023) <doi:10.3390/fractalfract7090641>, and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) <doi:10.48550/arXiv.2110.05316>, which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models.

Waldir Leoncio

EMJMCMC

Evolutionary Mode Jumping Markov Chain Monte Carlo Expert
Toolbox

Aliaksandr Hubin

Geir Storvik

Florian Frommlet

m function

<dl><dt>a</dt>
<dd>the first argument</dd>
<dt>b</dt>
<dd>the second argument</dd></dl>

Arguments

Product function used in the deep regression context — m

<dl>

<dt>a</dt>
<dd>the first argument</dd>


<dt>b</dt>
<dd>the second argument</dd>

</dl>

m: Product function used in the deep regression context

Description

Usage

Value

Arguments

Examples