distfun_to_dist

Function to define output distribution based on dist_fun

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

distfun_to_dist function

<dl><dt>dist_fun</dt>
<dd>a distribution function as defined by <code>make_tfd_dist</code></dd>
<dt>preds</dt>
<dd>tensors with predictions</dd></dl>

Arguments

Function to define output distribution based on dist_fun — distfun_to_dist

<dl>

<dt>dist_fun</dt>
<dd>a distribution function as defined by <code>make_tfd_dist</code></dd>


<dt>preds</dt>
<dd>tensors with predictions</dd>

</dl>

distfun_to_dist: Function to define output distribution based on dist_fun

Description

Usage

Value

Arguments