softmax

dsoftmax

Softmax for a vector x is defined as

Define distribution families and fit them to
interval-censored and interval-truncated data, where the truncation
bounds may depend on the individual observation. The defined
distributions feature density, probability, sampling and fitting
methods as well as efficient implementations of the log-density log
f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
neural networks via the 'tensorflow' package. Allows training
parametric neural networks on interval-censored and interval-truncated
data with flexible parameterization. Applications include Claims
Development in Non-Life Insurance, e.g. modelling reporting delay
distributions from incomplete data, see Bücher, Rosenstock (2022)
<doi:10.1007/s13385-022-00314-4>.

Alexander Rosenstock

reservr

Fit Distributions and Neural Networks to Censored and Truncated
Data

softmax function

<dl><dt>x</dt>
<dd>A numeric vector or matrix</dd></dl>

Arguments

Soft-Max function — softmax

<dl>

<dt>x</dt>
<dd>A numeric vector or matrix</dd>

</dl>

softmax: Soft-Max function

Description

Usage

Value

Arguments

Details

Examples