quants

Using the built in quantile function to return 95% confidence interval

Latent binary Bayesian neural networks (LBBNNs) are implemented using
'torch', an R interface to the LibTorch backend. Supports mean-field variational
inference as well as flexible variational posteriors using normalizing flows.
The standard LBBNN implementation follows Hubin and Storvik (2024) <doi:10.3390/math12060788>,
using the local reparametrization trick as in Skaaret-Lund et al. (2024)
<https://openreview.net/pdf?id=d6kqUKzG3V>. Input-skip connections are also supported,
as described in Høyheim et al. (2025) <doi:10.48550/arXiv.2503.10496>.

Lars Skaaret-Lund

LBBNN

Latent Binary Bayesian Neural Networks Using 'torch'

Aliaksandr Hubin

Eirik Høyheim

quants function

<dl><dt>x</dt>
<dd>numeric vector whose sample quantiles is desired.</dd></dl>

Arguments

Function to obtain empirical 95% confidence interval, including the mean — quants

<dl>

<dt>x</dt>
<dd>numeric vector whose sample quantiles is desired.</dd>

</dl>

quants: Function to obtain empirical 95% confidence interval, including the mean

Description

Usage

Value

Arguments