We describe computationally \u000eefficient methods for Bayesian model selection. The methods select among mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs), and can be applied to data sets in which some of the random variables are not always observed. The model-selection criterion that we consider is the posterior probability of the model (structure) given data. Our model-selection problem is di\u000ecult because (1) the number of possible model structures grows super-exponentially with the number of random variables and (2) missing data necessitates the use of computationally slow approximations of model posterior probability. We argue that simple search-and-score algorithms are infeasible for a variety of problems, and introduce a feasible approach in which parameter and structure search is interleaved and expected data is treated as real data. Our approach can be viewed as a combination of the Cheeseman Stutz asymptotic approximation for model posterior probability and the Expectation Maximization algorithm. We evaluate our procedure for selecting among MDAGs on synthetic and real examples.<\/p>\n","protected":false},"excerpt":{"rendered":"
We describe computationally \u000eefficient methods for Bayesian model selection. The methods select among mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs), and can be applied to data sets in which some of the random variables are not always observed. 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