Bayesian Inference of Regular Grammar and Markov Source Models

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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Kurt Smith, Michael Miller


In this paper we develop a Bayes criterion which includes the Rissanen complexity, for inferring regular grammar models. We develop two methods for regular grammar Bayesian inference. The fIrst method is based on treating the regular grammar as a I-dimensional Markov source, and the second is based on the combinatoric characteristics of the regular grammar itself. We apply the resulting Bayes criteria to a particular example in order to show the efficiency of each method.