risk

computes a Monte Carlo approximation of an estimator's Bayes risk

An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data.

Matthew Sainsbury-Dale

NeuralEstimators

Likelihood-Free Parameter Estimation using Neural Networks

risk function

<dl><dt>assessment</dt>
<dd>an object returned by <code>assess()</code></dd>
<dt>loss</dt>
<dd>a binary operator defining the loss function (default absolute-error loss)</dd>
<dt>average_over_parameters</dt>
<dd>if <code>TRUE</code>, the loss is averaged over all parameters; otherwise (default), the loss is averaged over each parameter separately</dd>
<dt>average_over_sample_sizes</dt>
<dd>if <code>TRUE</code> (default), the loss is averaged over all sample sizes (the column <code>m</code> in <code>df</code>); otherwise, the loss is averaged over each sample size separately</dd></dl>

Arguments

computes a Monte Carlo approximation of an estimator's Bayes risk — risk

<dl>

<dt>assessment</dt>
<dd>an object returned by <code>assess()</code></dd>


<dt>loss</dt>
<dd>a binary operator defining the loss function (default absolute-error loss)</dd>


<dt>average_over_parameters</dt>
<dd>if <code>TRUE</code>, the loss is averaged over all parameters; otherwise (default), the loss is averaged over each parameter separately</dd>


<dt>average_over_sample_sizes</dt>
<dd>if <code>TRUE</code> (default), the loss is averaged over all sample sizes (the column <code>m</code> in <code>df</code>); otherwise, the loss is averaged over each sample size separately</dd>

</dl>

risk: computes a Monte Carlo approximation of an estimator's Bayes risk

Description

Usage

Value

Arguments

See Also