Overview

To develop a neural estimator with NeuralEstimators,

• Sample parameters from the prior distribution. The parameters are stored as $p \times K$ matrices, with $p$ the number of parameters in the model and $K$ the number of parameter vectors in the given parameter set (i.e., training, validation, or test set).
• Simulate data from the assumed model over the parameter sets generated above. These data are stored as a Vector{A}, with each element of the vector associated with one parameter configuration, and where A depends on the multivariate structure of the data and the representation of the neural estimator (e.g., an Array for CNN-based estimators, a GNNGraph for GNN-based estimators, etc.).
• Initialise a neural network θ̂.
• Train θ̂ under the chosen loss function using train().
• Assess θ̂ using assess(), which uses simulation-based methods to assess the estimator with respect to its sampling distribution.

Once the estimator θ̂ has passed our assessments and is therefore deemed to be well calibrated, it may be applied to observed data. See the Examples and, once familiar with the basic workflow, see Advanced usage for practical considerations on how to most effectively construct neural estimators.