{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["18(6)"],"submitter":["Bello L"],"pubmed_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 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 gener"],"journal":["PloS one"],"pagination":["e0286074"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10243634"],"repository":["biostudies-literature"],"pubmed_title":["Compressed computations using wavelets for hidden Markov models with continuous observations."],"pmcid":["PMC10243634"],"pubmed_authors":["Wiedenhoft J","Bello L","Schliep A"],"additional_accession":[]},"is_claimable":false,"name":"Compressed computations using wavelets for hidden Markov models with continuous observations.","description":"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 gener","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-04-19T14:14:58.33Z","creation":"2025-04-19T14:14:58.33Z"},"accession":"S-EPMC10243634","cross_references":{"pubmed":["37279196"],"doi":["10.1371/journal.pone.0286074"]}}