Gallstone_Dataset

<p>Taken from the UCI machine learning repository. The task is
to classify whether the patient had gallstones or not. It contains a mix
of demographic data and bioimpedance data.</p>

datasets

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

Gallstone_Dataset function

<p>This dataset has 319 rows and  38 columns.</p>

Gallstone_Dataset: Gallstone Dataset

Description

Usage

Arguments

Format