{"doi":"10.1155/2014/231704","title":"A Novel Method for Decoding Any High-Order Hidden Markov Model","abstract":"<jats:p>This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.</jats:p>","journal":"Discrete Dynamics in Nature and Society","year":2014,"id":29514,"datarank":0.41184326231207957,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.14307934192787133,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.14307934192787133,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":5,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":24031,"name":"Yifei Wang","orcid":"0000-0002-3313-0974","position":1,"is_corresponding":false},{"id":162584,"name":"Fei Ye","orcid":"0000-0001-8206-542X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24523987","pmcid":null,"openalex_id":"https://openalex.org/W2161328464","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"71390521","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"2014M551565","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"2012tlxyrc04","title":null}],"total_grants":3,"fwci":1.2684,"citation_percentile":0.85900285,"influential_citations":0,"citation_trend":[{"year":2015,"count":1},{"year":2016,"count":1},{"year":2017,"count":1},{"year":2020,"count":1},{"year":2024,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://downloads.hindawi.com/journals/ddns/2014/231704.pdf","host_type":"journal"},{"url":"https://downloads.hindawi.com/journals/ddns/2014/231704.pdf","host_type":"GOLD"},{"url":"https://downloads.hindawi.com/journals/ddns/2014/231704.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/ddns/2014/231704.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/ddns/2014/231704.xml","host_type":"publisher"},{"url":"https://doi.org/10.1155/2014/231704","host_type":"journal"},{"url":"http://downloads.hindawi.com/journals/DDNS/2014/231704.xml","host_type":"repository"},{"url":"https://doaj.org/article/4a0690cfd66843459c076acda032f63c","host_type":"repository"}],"fields_of_study":["Bayesian Modeling and Causal Inference","DNA and Biological Computing","Maritime Navigation and Safety","Computer Science"],"mesh_terms":[],"keywords":["Hidden Markov model","Forward algorithm","Markov model","Hidden semi-Markov model","Viterbi algorithm","Maximum-entropy Markov model","Markov chain","Variable-order Markov model","Computer science","Sequence (biology)","Markov property","Sequence labeling","Markov process","Decoding methods","Algorithm","Mathematics","Artificial intelligence","Machine learning","Statistics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-09T00:02:51.760382Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}