Score functions
BIC score
Calculate the local score with BIC for the linear Gaussian case.
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
parameters: None.
Returns
score: Local BIC score.
- BIC
Schwarz, Gideon. “Estimating the dimension of a model.” The annals of statistics (1978): 461-464.
BDeu score
Calculate the local score with BDeu for the discrete case.
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
- parameters:
sample_prior: sample prior.
structure_prior: structure prior.
r_i_map: number of states of the finite random variable ‘X_{i}’.
Returns
score: Local BDeu score.
- BDeu
Buntine, Wray. “Theory refinement on Bayesian networks.” Uncertainty proceedings 1991. Morgan Kaufmann, 1991. 52-60.
Generalized score with cross validation
Calculate the local score using negative k-fold cross-validated log likelihood as the score, based on a regression model in RKHS [GeneralizedScore].
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
- parameters:
kfold: the fold number in cross validation.
lambda: regularization parameter.
Returns
score: Local score.
Generalized score with marginal likelihood
Calculate the local score by negative marginal likelihood, based on a regression model in RKHS [GeneralizedScore].
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
parameters: None.
Returns
score: Local score.
Generalized score with cross validation for multi-dimensional variables
Calculate the local score using negative k-fold cross-validated log likelihood as the score, based on a regression model in RKHS for data with multi-dimensional variables [GeneralizedScore].
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
- parameters:
kfold: the fold number in cross validation.
lambda: regularization parameter.
dlabel: indicate the data dimensions that belong to each variable. It is only used when the variables have multivariate dimensions.
Returns
score: Local score.
Generalized score with marginal likelihood for multi-dimensional variables
Calculate the local score by negative marginal likelihood, based on a regression model in RKHS for data with multi-dimensional variables [GeneralizedScore].
Parameters
Data: (sample, features).
i: current index.
PAi: parent indexes.
- parameters:
dlabel: indicate the data dimensions that belong to each variable. It is only used when the variables have multivariate dimensions.
Returns
score: Local score.