Predict Confidence Intervals via Last-Layer Laplace Approximation

Predict-time entry point for regression confidence intervals. Called by parsnip via c(pkg = "kerasnip", fun = "laplace_conf_int_reg").

For each output in the model, this builds the per-sample epistemic variance (uncertainty on E[Y|X]) from the stored Laplace posterior and returns symmetric Normal-based intervals at the requested confidence level.

Usage

laplace_conf_int_reg(object, x, laplace_data, level = 0.95)

Arguments

object

The raw Keras model (from object$fit$fit).

x

Processed predictor data (matrix or array).

laplace_data

A named list of Laplace posterior data, one entry per output (from object$fit$laplace). Each entry contains h_diag, tau, sigma_sq_noise, n_training, and combined_model.

level

Confidence level (default 0.95). Passed through from predict(..., type = "conf_int", level = 0.95).

Value

A named list of matrices (one per output), each with columns .pred, .pred_lower, and .pred_upper.