NeuralEstimators - Likelihood-Free Parameter Estimation using Neural Networks
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.
