Abstract:Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms—Forward Filtering, Backward Smoothing and Viterbi—and Gibbs sampling for Bayesian HMM. For Bayesian HMM with cont… Show more
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