Minimum Message Length Hidden Markov Modelling
نویسندگان
چکیده
This paper describes a Minimum Message Length (MML) approach to nding the most appropriate Hidden Markov Model (HMM) to describe a given sequence of observations. A MML estimate for the expected length of a two-part message stating a speci c HMM and the observations given this model is presented along with an e ective search strategy for nding the best number of states for the model. The information estimate enables two models with di erent numbers of states to be fairly compared which is neccessary if the search of this complex model space is to avoid the worst locally optimal solutions. The general purpose MML classi er `Snob' has been extended and the new program `tSnob' is tested on `synthetic' data and a large `real world' dataset. The MML measure is found to be an improvement on the Bayesian Information Criteria (BIC) and the un-supervised search strategy e ective.
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