Probabilistic Graphical Markov Model Learning: An Adaptive Strategy
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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In this paper a new model random sampler algorithm, only based on the interaction structure of the model is presented. It means that the vector values of the parameters of the distribution are not needed to perform the sample generation. The algorithm is tested generating nine structure models of 10, 12, and 14 variables, and conditional independence restrictions with structures, sparse, mean and dense. Eight random samples are generated from each structure model, for a total of 72 random samples. To validate the results an external criterion is used. Every sample is given to the model selection algorithm implemented in MIM software, which obtains the structure of the departure model for 93% of the samples. In all cases the generation time of a sample was not greater than 4 minutes. The mean run time grows with the density of the models. The MSRS algorithm converges in at most 4 iterations.