Workflow overview

The typical workflow when using the package is as follows:

1. Sample parameters (from the prior or proposal distribution) to form training/validation/test parameter sets. Parameters are typically stored as   matrices, where is the dimension of and is the number of parameter vectors in the given parameter set, though any batchable object is supported.

2. Simulate data from the model conditional on the above parameter sets, to form training/validation/test data sets. Simulated data sets are stored as batches in a format amenable to the chosen neural-network architecture (see Step 3).

3. Construct a neural network that maps data sets to a   matrix of summary statistics for , where is user-specified. The architecture class (e.g., MLP, CNN, GNN, DeepSet) should reflect the structure of the data (e.g., unstructured, grid, graph, replicated). Any Flux model can be used.

4. Construct a NeuralEstimator by wrapping the neural network in the subtype corresponding to the intended inferential method (PointEstimator, PosteriorEstimator, RatioEstimator).

5. Train the NeuralEstimator using train and the training set, monitoring performance and convergence using the validation set.

6. Assess the NeuralEstimator using assess and the test set.

7. Apply the NeuralEstimator to observed data (see Inference with observed data).

For a minimal working example, see Quick start.