loadstate

Load a saved state of a neural estimator, including both weights and additional model parameters.

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

loadstate function

<dl><dt>estimator</dt>
<dd>the neural estimator that we wish to load the state into</dd>
<dt>filename</dt>
<dd>file (including absolute path) of the neural-network state in a <code>bson</code> file</dd></dl>

Arguments

load a saved state of a neural estimator — loadstate

<dl>

<dt>estimator</dt>
<dd>the neural estimator that we wish to load the state into</dd>


<dt>filename</dt>
<dd>file (including absolute path) of the neural-network state in a <code>bson</code> file</dd>

</dl>

loadstate: load a saved state of a neural estimator

Description

Usage

Value

Arguments