AIC.tulpa_fit: Information criteria on a tulpa fit

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Information criteria on a tulpa fit

Description

AIC and BIC penalise a maximised log-likelihood. logLik.tulpa_fit() reports one only for a fit that maximised over every parameter it reports (quantity = "log_likelihood", as agq_fit() does); a mean log posterior, a log evidence and a conditional log marginal likelihood are not, and both criteria refuse them rather than return a number. Compare those fits by their evidence, or by compare_models(criterion = "waic") / "loo".

Usage

## S3 method for class 'tulpa_fit'
AIC(object, ..., k = 2)

## S3 method for class 'tulpa_fit'
BIC(object, ...)

Arguments

object	A tulpa_fit object.
...	Further fits.
k	Penalty per parameter.

Value

As stats::AIC() / stats::BIC() for maximised log-likelihoods; errors otherwise.

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.