{"id":"https://openalex.org/W3012839431","doi":"https://doi.org/10.1007/s00180-021-01102-6","title":"Direct statistical inference for finite Markov jump processes via the matrix exponential","display_name":"Direct statistical inference for finite Markov jump processes via the matrix exponential","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3012839431","doi":"https://doi.org/10.1007/s00180-021-01102-6","mag":"3012839431","pmid":"https://pubmed.ncbi.nlm.nih.gov/33897113"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-021-01102-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-021-01102-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-021-01102-6.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-021-01102-6.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052870306","display_name":"Chris Sherlock","orcid":"https://orcid.org/0000-0002-2429-3157"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Chris Sherlock","raw_affiliation_strings":["Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","LANCASTER UNIVERSITY,"],"raw_orcid":"https://orcid.org/0000-0002-2429-3157","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"LANCASTER UNIVERSITY,","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052870306"],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.0813,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41493754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"36","issue":"4","first_page":"2863","last_page":"2887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-exponential","display_name":"Matrix exponential","score":0.6420058012008667},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5777075290679932},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.567408561706543},{"id":"https://openalex.org/keywords/exponentiation","display_name":"Exponentiation","score":0.5004541873931885},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4721866846084595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45622923970222473},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4316387176513672},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.4266239106655121},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42615509033203125},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4244350790977478},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4235243797302246},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.41390833258628845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15629607439041138},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12220498919487},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09238719940185547}],"concepts":[{"id":"https://openalex.org/C195906000","wikidata":"https://www.wikidata.org/wiki/Q1191722","display_name":"Matrix exponential","level":3,"score":0.6420058012008667},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5777075290679932},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.567408561706543},{"id":"https://openalex.org/C81539297","wikidata":"https://www.wikidata.org/wiki/Q33456","display_name":"Exponentiation","level":2,"score":0.5004541873931885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4721866846084595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45622923970222473},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4316387176513672},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.4266239106655121},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42615509033203125},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4244350790977478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4235243797302246},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.41390833258628845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15629607439041138},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12220498919487},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09238719940185547},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.0}],"mesh":[],"locations_count":8,"locations":[{"id":"doi:10.1007/s00180-021-01102-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-021-01102-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-021-01102-6.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmid:33897113","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33897113","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational statistics","raw_type":null},{"id":"pmh:oai:arXiv.org:1809.07110","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.07110","pdf_url":"https://arxiv.org/pdf/1809.07110","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:eprints.lancs.ac.uk:153753","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/153753/1/MatExp.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},{"id":"mag:3012839431","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1809.07110.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01102-6","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s00180-021-01102-6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:8054858","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8054858","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Comput Stat","raw_type":"Text"},{"id":"doi:10.48550/arxiv.1809.07110","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1809.07110","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s00180-021-01102-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-021-01102-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-021-01102-6.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3012839431.pdf","grobid_xml":"https://content.openalex.org/works/W3012839431.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W755194005","https://openalex.org/W1499965417","https://openalex.org/W1501586228","https://openalex.org/W1526841711","https://openalex.org/W1608691223","https://openalex.org/W1987625453","https://openalex.org/W1990647940","https://openalex.org/W2002409557","https://openalex.org/W2003456014","https://openalex.org/W2008620264","https://openalex.org/W2009743150","https://openalex.org/W2012459404","https://openalex.org/W2018259857","https://openalex.org/W2022667068","https://openalex.org/W2028212620","https://openalex.org/W2029927128","https://openalex.org/W2030426219","https://openalex.org/W2037740528","https://openalex.org/W2042579756","https://openalex.org/W2044543226","https://openalex.org/W2057602562","https://openalex.org/W2069596643","https://openalex.org/W2074711122","https://openalex.org/W2083895788","https://openalex.org/W2093517417","https://openalex.org/W2101958901","https://openalex.org/W2106201557","https://openalex.org/W2106565812","https://openalex.org/W2114134972","https://openalex.org/W2122028584","https://openalex.org/W2123726866","https://openalex.org/W2125488105","https://openalex.org/W2130727150","https://openalex.org/W2138192576","https://openalex.org/W2150217546","https://openalex.org/W2153587490","https://openalex.org/W2168282770","https://openalex.org/W2177469511","https://openalex.org/W2233986972","https://openalex.org/W2412589610","https://openalex.org/W2485609644","https://openalex.org/W2582743722","https://openalex.org/W2784861862","https://openalex.org/W2789802022","https://openalex.org/W2799087070","https://openalex.org/W2802799492","https://openalex.org/W2890547940","https://openalex.org/W2945961828","https://openalex.org/W2962916849","https://openalex.org/W2998952331","https://openalex.org/W3123235823","https://openalex.org/W3124477112","https://openalex.org/W4240303976","https://openalex.org/W4245577611"],"related_works":["https://openalex.org/W3033436096","https://openalex.org/W3035019903","https://openalex.org/W3211448181","https://openalex.org/W3128611278","https://openalex.org/W3194610957","https://openalex.org/W2950857184","https://openalex.org/W1989882934","https://openalex.org/W2766911867","https://openalex.org/W3093237553","https://openalex.org/W2005630497","https://openalex.org/W3016582770","https://openalex.org/W2891148009","https://openalex.org/W3094434235","https://openalex.org/W2181356457","https://openalex.org/W2076020996","https://openalex.org/W2099994229","https://openalex.org/W1547324788","https://openalex.org/W2112321969","https://openalex.org/W2904254030","https://openalex.org/W1566277302"],"abstract_inverted_index":{"Given":[0],"noisy,":[1],"partial":[2],"observations":[3],"of":[4,47,124,127,134,153,162,172],"a":[5,61,77,104,128,135,167,176],"time-homogeneous,":[6],"finite-statespace":[7],"Markov":[8,74],"chain,":[9],"conceptually":[10],"simple,":[11],"direct":[12],"statistical":[13,70,160],"inference":[14,71,93],"is":[15,37,83,108],"available,":[16],"in":[17,53,97,175],"theory,":[18],"via":[19,86],"its":[20],"rate":[21,138],"matrix,":[22],"or":[23,60,89],"infinitesimal":[24],"generator,":[25],"<mml:math":[26,30,100],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi>Q</mml:mi></mml:math>":[27,101],",":[28],"since":[29],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mrow><mml:mo>exp</mml:mo>":[31],"<mml:mo>(</mml:mo>":[32],"<mml:mi>Q</mml:mi>":[33],"<mml:mi>t</mml:mi>":[34],"<mml:mo>)</mml:mo></mml:mrow>":[35],"</mml:math>":[36],"the":[38,64,125,132,158,163,170,180],"transition":[39],"matrix":[40,51],"over":[41],"time":[42],"<i>t</i>.":[43],"However,":[44],"perhaps":[45],"because":[46],"inadequate":[48],"tools":[49,149],"for":[50,72,169],"exponentiation":[52],"programming":[54],"languages":[55],"commonly":[56],"used":[57],"amongst":[58],"statisticians":[59],"belief":[62],"that":[63,150],"necessary":[65],"calculations":[66],"are":[67],"prohibitively":[68],"expensive,":[69],"continuous-time":[73],"chains":[75],"with":[76,131],"large":[78],"but":[79],"finite":[80],"state":[81],"space":[82],"typically":[84],"conducted":[85],"particle":[87],"MCMC":[88],"other":[90],"relatively":[91,143],"complex":[92],"schemes.":[94],"When,":[95],"as":[96],"many":[98],"applications":[99],"arises":[102],"from":[103],"reaction":[105],"network,":[106],"it":[107],"usually":[109],"sparse.":[110],"We":[111,156],"describe":[112],"variations":[113],"on":[114,166,179],"known":[115],"algorithms":[116],"which":[117],"allow":[118],"fast,":[119],"robust":[120],"and":[121,178],"accurate":[122],"evaluation":[123],"product":[126],"non-negative":[129],"vector":[130],"exponential":[133],"large,":[136],"sparse":[137],"matrix.":[139],"Our":[140],"implementation":[141],"uses":[142],"recently":[144],"developed,":[145],"efficient,":[146],"linear":[147],"algebra":[148],"take":[151],"advantage":[152],"such":[154],"sparsity.":[155],"demonstrate":[157],"straightforward":[159],"application":[161],"key":[164],"algorithm":[165],"model":[168],"mixing":[171],"two":[173],"alleles":[174],"population":[177],"Susceptible-Infectious-Removed":[181],"epidemic":[182],"model.":[183]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
