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Example prior for a Bayesian calibration model

Source: R/bayes.R
calibration_prior.Rd

Builds a weakly informative prior for a calibration formula, centred on the hypothesis that the BirdNET score is already calibrated: on the model scale (tp ~ logit_conf), intercept 0 and slope 1 mean P(TP) = score. The data then show how far the classifier departs from it. Only the classes present in the formula are returned:

Usage

calibration_prior(formula, validations)

Arguments

formula

Calibration formula (see calibration_models()).

validations

Table returned by read_validations().

Value

A brmsprior object.

Details

  • Intercept: normal(m, 1.5), where m is the mean of logit_conf among the reviewed clips. 'brms' centres the predictors, so its intercept is the logit of P(TP) at the mean logit score; with a slope of 1, an intercept equal to that mean gives P(TP) = score;

  • b (slope of logit_conf): normal(1, 1) truncated at 0, centred on a calibrated score, wide enough to allow slopes from near 0 to near 3, and assuming that a higher score never lowers P(TP);

  • sd (standard deviations of the random effects): exponential(1);

  • cor (correlations between random effects): lkj(2), which pulls the correlations away from +-1 (the singular fits of m4 with 'lme4').

This is an example, not a default: fit_calibration() with engine = "brms" requires the prior to be given explicitly. Check what it implies on the probability scale with prior_predictive_calibration() before fitting, and its influence on the posterior with calibration_sensitivity(). On weakly informative priors and prior predictive checks see McElreath (2020, doi:10.1201/9780429029608 ) and Gelman et al. (2026, Bayesian Workflow, Chapman & Hall).