sigmoid

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

sigmoid function

<dl><dt>x</dt>
<dd>a real number</dd></dl>

Arguments

sigmoid activation function — sigmoid

<dl>

<dt>x</dt>
<dd>a real number</dd>

</dl>

sigmoid activation function

sigmoid: sigmoid activation function

Description

Usage

Value

Arguments

Examples