bias

computes a Monte Carlo approximation of an estimator's bias

An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural point estimators, which are neural networks that map data to a point summary of the posterior distribution. These estimators are likelihood-free and amortised, in the sense that, after an initial setup cost, inference from observed data can be made in a fraction of the time required by conventional approaches; see Sainsbury-Dale, Zammit-Mangion, and Huser (2024) <doi:10.1080/00031305.2023.2249522> for further details and an accessible introduction. The package also enables the construction of neural networks that approximate the likelihood-to-evidence ratio in an amortised manner, allowing one to perform inference based on the likelihood function or the entire posterior distribution; see Zammit-Mangion, Sainsbury-Dale, and Huser (2024, Sec. 5.2) <doi:10.48550/arXiv.2404.12484>, and the references therein. The package accommodates any model for which simulation is feasible by allowing the user to implicitly define their model through simulated data.

Matthew Sainsbury-Dale

NeuralEstimators

Likelihood-Free Parameter Estimation using Neural Networks

bias function

<dl><dt>assessment</dt>
<dd>an object returned by <code>assess()</code> (or the <code>estimates</code> data frame of this object)</dd>
<dt>...</dt>
<dd>optional arguments inherited from <code>risk</code> (excluding the argument <code>loss</code>)</dd></dl>

Arguments

computes a Monte Carlo approximation of an estimator's bias — bias

<dl>

<dt>assessment</dt>
<dd>an object returned by <code>assess()</code> (or the <code>estimates</code> data frame of this object)</dd>


<dt>...</dt>
<dd>optional arguments inherited from <code>risk</code> (excluding the argument <code>loss</code>)</dd>

</dl>

bias: computes a Monte Carlo approximation of an estimator's bias

Description

Usage

Value

Arguments

See Also