Likelihood-Free Parameter Estimation using Neural Networks.

NeuralEstimators

This repository contains the R interface to the Julia package NeuralEstimators. The package facilitates a suite of neural methods for parameter inference in scenarios where simulation from the model is feasible. These methods are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, they enable rapid inference across arbitrarily many observed data sets in a fraction of the time required by conventional approaches. The package caters for any model for which simulation is feasible by allowing the user to implicitly define their model via simulated data.

See the Julia documentation or the vignette to get started!

Installation

To install the package, please:

1. Install required software
   Ensure you have both Julia and R installed on your system.

2. Install the Julia version of NeuralEstimators

   • To install the current stable version, run the following command in your terminal:

     julia -e 'using Pkg; Pkg.add("NeuralEstimators")'
     

   • To install the development version, run:

     julia -e 'using Pkg; Pkg.add(url="https://github.com/msainsburydale/NeuralEstimators.jl")'
     

3. Install the R interface to NeuralEstimators

   • To install from CRAN, run the following command in R:

     install.packages("NeuralEstimators")
     

   • To install the development version, first ensure you have devtools installed, then run:

     devtools::install_github("msainsburydale/NeuralEstimators")
     

Supporting and citing

This software was developed as part of academic research. If you would like to support it, please star the repository. If you use the software in your research or other activities, please use the citation information accessible with the command:

citation("NeuralEstimators")

Contributing

If you encounter a bug or have a suggestion, please consider opening an issue or submitting a pull request. Instructions for developing vignettes can be found in vignettes/README.md.

Papers using NeuralEstimators

• Likelihood-free parameter estimation with neural Bayes estimators[paper][code]

• Neural methods for amortized inference[paper][code]

• Neural Bayes estimators for irregular spatial data using graph neural networks[paper][code]

• Neural Bayes estimators for censored inference with peaks-over-threshold models[paper][code]

• Neural parameter estimation with incomplete data[paper][code]