IIAMB Algorithm

Overview

Interleaved IAMB (IIAMB) is a constraint-based local discovery algorithm that learns Markov blankets using an interleaved grow-and-shrink strategy. Unlike the sequential Grow-Shrink algorithm — which fully completes the grow phase before starting the shrink phase — IIAMB alternates between adding the most associated variable and immediately removing any variable in the current candidate set that has become conditionally independent. This interleaving reduces the number of conditional independence tests required and tends to produce cleaner intermediate blankets.

IIAMB is generally more test-efficient than GS on datasets with many variables, making it a good choice when computational cost of CI testing is a concern.

Class: constraint · DAG Package: bnlearn

Reference

Tsamardinos I., Aliferis C.F. & Statnikov A. (2003) – Algorithms for Large Scale Markov Blanket Discovery. FLAIRS 2003, 376–380.

Hyperparameters

Hyperparameter	Type	Default	Values	Description
alpha	float	0.05	—	p-value threshold below which a CI test indicates conditional independence.
ci_test	str	mi	mi, x2	Conditional independence test used in constraint-based learning.
max_elapsed	int	No limit	—	Maximum allowed execution time in seconds.

Variants

Variant	Package
bnlearn	bnlearn