bayesian_ridge_regression — Logtalk 3.100.1

object

# `bayesian_ridge_regression`

Bayesian ridge regression regressor supporting continuous and mixed-feature datasets using evidence maximization and posterior uncertainty over coefficients. Learns from a dataset object implementing the `regression_dataset_protocol` protocol and returns a regressor term that can be used for prediction, predictive-distribution queries, and export as predicate clauses.

Availability:
`logtalk_load(bayesian_ridge_regression(loader))`
Author: Paulo Moura
Version: 1:0:0
Date: 2026-05-07
Compilation flags:
`static, context_switching_calls`
Imports:
`public` regressor_common
Uses:
format
linear_algebra
list
numberlist
population
type
Remarks:
(none)
Inherited public predicates:
check_option/1 check_options/1 check_regressor/1 default_option/1 default_options/1 diagnostic/2 diagnostics/2 export_to_clauses/4 export_to_file/4 learn/2 learn/3 option/2 option/3 predict/3 print_regressor/1 regressor_options/2 valid_option/1 valid_options/1 valid_regressor/1

## Public predicates

### `predict_distribution/3`

Predicts the posterior predictive Gaussian distribution for a new instance using the learned regressor. The returned term has the shape `gaussian(Mean, Variance)` where `Variance` includes the learned observation noise variance and coefficient posterior uncertainty only; the intercept is not treated as a probabilistic parameter.

Compilation flags:
`static`
Template:
`predict_distribution(Regressor,Instance,Distribution)`
Mode and number of proofs:
`predict_distribution(+compound,+list,-compound)` - `one`

### `weight_variances/2`

Returns the posterior marginal variances of the encoded feature coefficients in encoder order. Encoded features dropped from fitting because they had zero variance are reported with posterior variance zero.

Compilation flags:
`static`
Template:
`weight_variances(Regressor,Variances)`
Mode and number of proofs:
`weight_variances(+compound,-list(float))` - `one`

## Protected predicates

(no local declarations; see entity ancestors if any)

## Private predicates

(no local declarations; see entity ancestors if any)

## Operators

(none)

See also

linear_regression, ridge_regression, lasso_regression, elastic_net_regression, gaussian_process_regression, knn_regression, regression_tree, random_forest_regression, gradient_boosting_regression