This module implements the BGe (Bayesian Gaussian Equivalent) score for evaluating Bayesian networks.
 
The BGe score is used to compute the marginal likelihood of a Bayesian network given data. It supports operations
such as computing the score for the entire graph, individual nodes, and edges. The implementation includes
parameters for regularization and scoring.
 
Classes:
    BGeScore: Implements the BGe score computation.

Modules
		numpy pandas

Classes
		structure_learning.scores.score.Score(abc.ABC) BGeScore   class BGeScore(structure_learning.scores.score.Score)     BGeScore(data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame])   BGe (Bayesian Gaussian Equivalent) Score     Method resolution order: BGeScore structure_learning.scores.score.Score abc.ABC builtins.object Methods defined here: __init__(self, data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame]) Initialise BGe instance.   Parameters:     data (Data | pandas.DataFrame): data compute(self, graph: structure_learning.data_structures.graph.Graph) Compute the BGE for the data   Returns:     (dict): score and parameters compute_node_with_edges(self, node: str, parents: list, node_index_map: dict) Compute the BGE for edge(s)   Parameter:     node (str): node label     parents (list (str)): node labels of parent nodes   Returns:     (dict): score and parameters Data descriptors defined here: am parameters reg_coefficients Data and other attributes defined here: __abstractmethods__ = frozenset() Methods inherited from structure_learning.scores.score.Score: compute_node(self, graph: structure_learning.data_structures.graph.Graph, node: str) Implements a score function (e.g. BGe, Marginal Likelihood, etc) for a specific node Readonly properties inherited from structure_learning.scores.score.Score: node_labels Data descriptors inherited from structure_learning.scores.score.Score: __dict__ dictionary for instance variables __weakref__ list of weak references to the object data

Data
		Union = typing.Union lgamma = <ufunc 'loggamma'>