abi_estimate: Estimate parameters using trained neural estimator

In eam: Evidence Accumulation Models

Estimate parameters using trained neural estimator

Description

A wrapper around NeuralEstimators::estimate() that automatically extracts the trained estimator from a trained estimator object created by abi_train.

Usage

abi_estimate(trained_estimator, Z, X = NULL, batchsize = 32, use_gpu = TRUE)

Arguments

trained_estimator	A trained estimator object returned by abi_train. Must be of class eam_abi_trained_estimator and contain a trained_estimator element.
Z	Data in a format amenable to the neural-network architecture of estimator. Can be a single data set or a list of data sets.
X	Additional inputs to the neural network (default: NULL). If provided, the call will be of the form estimator((Z, X)).
batchsize	Integer; the batch size for applying estimator to Z (default: 32). Batching occurs only if Z is a list, indicating multiple data sets.
use_gpu	Logical; whether to use the GPU if available (default: TRUE).

Details

This function extracts the trained neural estimator from the trained estimator object and applies it to the provided data Z. The data Z should be in the same format as the summary statistics used during training (e.g., Z_train, Z_val, or Z_test from the ABI input).

Value

A matrix of outputs resulting from applying the trained estimator to Z (and possibly X).

Note

This function initializes the global Julia environment on first call.

Examples

## Not run: 
# Train an estimator first
trained_estimator <- abi_train(
  estimator = estimator,
  abi_input = abi_input,
  epochs = 100
)

# Estimate parameters for test data
point_est <- abi_estimate(
  trained_estimator = trained_estimator,
  Z = abi_input$Z_test[[1]]
)

# Estimate for multiple data sets
estimates <- abi_estimate(
  trained_estimator = trained_estimator,
  Z = abi_input$Z_test,
  batchsize = 16
)

## End(Not run)

eam documentation built on March 29, 2026, 5:07 p.m.