Score-class: Virtual Class "Score"
Description
This virtual base class represents a score for causal inference; it is used
in the causal inference functions ges, gies and
simy.Details
Score-based structure learning algorithms for causal inference such as
Greedy Equivalence Search
(GES, implemented in the function ges), Greedy Interventional
Equivalence Search (GIES, implemented in the function gies) and
the dynamic programming approach of Silander and Myllymäki (2006)
(implemented in the function simy) try to find the DAG model which
maximizes a scoring criterion for a given data set. A widely-used scoring
criterion is the Bayesian Information Criterion (BIC).
The virtual class Score is the base class for providing a scoring
criterion to the mentioned causal inference algorithms. It does not
implement a concrete scoring criterion, but it defines the functions that
must be provided by its descendants (cf. methods).
Knowledge of this class is only required if you aim to implement an own
scoring criterion. At the moment, it is recommended to use the predefined
scoring criteria for multivariate Gaussian data derived from Score,
GaussL0penIntScore and
GaussL0penObsScore.References
T. Silander and P. Myllymäki (2006). A simple approach for finding the
globally optimal Bayesian network structure. Proceedings of the 22nd
Conference on Uncertainty in Artificial Intelligence (UAI 2006), 445--452See Also
ges, gies, simy,
GaussL0penIntScore,
GaussL0penObsScore