biostudies-literatureUnknown18(6)Bello LCompression 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 continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generPloS onee0286074https://www.ebi.ac.uk/biostudies/studies/S-EPMC10243634biostudies-literatureCompressed computations using wavelets for hidden Markov models with continuous observations.PMC10243634Wiedenhoft JBello LSchliep AfalseCompressed computations using wavelets for hidden Markov models with continuous observations.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 continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data gener2023-01-01T00:00:00Z20232025-04-19T14:14:58.33Z2025-04-19T14:14:58.33ZS-EPMC102436343727919610.1371/journal.pone.0286074