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Hill-Climbing Algorithm

Iterative optimization method progressively modifying the graph structure through local operations (edge addition, deletion, reversal) to improve the model's score.

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Simulated Annealing

Stochastic optimization algorithm occasionally allowing solution degradations to escape local optima, inspired by the metallurgical cooling process.

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D-Separation

Graphical criterion determining if two sets of variables are conditionally independent given a third set in a Bayesian network, based on path structure.

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Bayes Factor

Ratio of marginal likelihoods comparing two competing graphical models, quantifying the evidence from the data in favor of each hypothetical structure.

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Latent Structure

Graph configuration including unobserved (hidden) variables requiring specialized techniques like the EM algorithm for parameter and structure estimation.

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Structural Cross-Validation

Robust evaluation method partitioning data to estimate the predictive capability of different graph structures, preventing structural overfitting.

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Markov Chain Monte Carlo (MCMC)

Sampling technique exploring the space of structures according to their posterior probability, allowing approximation of the distribution over possible graphs.

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Exact Structure Inference

Analytical calculation of the posterior distribution over graphical structures, mathematically exact but exponentially complex in practice for more than a few variables.

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Moral Graph

Transformation of a directed graph into an undirected graph by connecting the parents of each node and removing the orientation, a prerequisite for certain inference methods.

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Structural Complexity

A measure quantifying the informational richness of a graph by its number of edges and parameters, balanced against its predictive power in selection criteria.

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