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Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. Therefore, the search space of Bayesian network structures may be organized in equivalence classes, where each of them represents a different set of conditional independencies. The collection of sets of(More)
Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are hidden. In earlier work, we have demonstrated in principle the possibility of reconstructing HLC models from data. We address the scalability issue and develop a search-based algorithm that can efficiently learn high-quality(More)
The search space of Bayesian Network struc­ tures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by lo­ cal transformations of DAGs. But the space of Bayesian Networks is ordered with respect to inclusion and it is natural to consider that a good search policy should take this into ac­ count. The first attempt to do this (Chick­(More)
The inclusion problem deals with how to characterize (in graphical terms) whether all independence statements in the model in­ duced by a DAG K are in the model induced by a second DAG L. Meek (1997) conjec­ tured that this inclusion holds iff there exists a sequence of DAGs from L to K such that only certain 'legal' arrow reversal and 'legal' arrow adding(More)
Narration and interaction are often viewed as contrary properties in computer games. Games with a high degree of interaction fail to provide a coherent narration and the player’s interaction seldom has any direct impact on the narrative. Games with a high degree of narration often tells a linear story similar to books or movies with little room for the(More)
There exist a lot of algorithms for the construction of Bayesian Networks (BN). But almost all computations in BN are carried out by transforming them to another special type of probabilistic models decomposable models (DM). This task of transformation is known to be a NP complex problem and todays algorithms for the construction of BN cannot guarantee the(More)
Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a(More)