bootstrap

Compute bootstrap estimates from a neural point estimator

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

bootstrap function

<dl><dt>estimator</dt>
<dd>a neural point estimator</dd>
<dt>Z</dt>
<dd>either a list of data sets simulated conditionally on the fitted parameters (parametric bootstrap); or a single observed data set containing independent replicates, which will be sampled with replacement <code>B</code> times (non-parametric bootstrap)</dd>
<dt>B</dt>
<dd>number of non-parametric bootstrap samples</dd>
<dt>blocks</dt>
<dd>integer vector specifying the blocks in non-parameteric bootstrap. For example, with 5 replicates, the first two corresponding to block 1 and the remaining three corresponding to block 2, <code>blocks</code> should be <code>c(1,1,2,2,2)</code></dd>
<dt>use_gpu</dt>
<dd>boolean indicating whether to use the GPU if it is available</dd></dl>

Arguments

bootstrap — bootstrap

<dl>

<dt>estimator</dt>
<dd>a neural point estimator</dd>


<dt>Z</dt>
<dd>either a list of data sets simulated conditionally on the fitted parameters (parametric bootstrap); or a single observed data set containing independent replicates, which will be sampled with replacement <code>B</code> times (non-parametric bootstrap)</dd>


<dt>B</dt>
<dd>number of non-parametric bootstrap samples</dd>


<dt>blocks</dt>
<dd>integer vector specifying the blocks in non-parameteric bootstrap. For example, with 5 replicates, the first two corresponding to block 1 and the remaining three corresponding to block 2, <code>blocks</code> should be <code>c(1,1,2,2,2)</code></dd>


<dt>use_gpu</dt>
<dd>boolean indicating whether to use the GPU if it is available</dd>

</dl>

bootstrap: bootstrap

Description

Usage

Value

Arguments