{"id":"https://openalex.org/W4390263611","doi":"https://doi.org/10.1007/s10994-023-06441-7","title":"Bayesian tensor factorisations for time series of counts","display_name":"Bayesian tensor factorisations for time series of counts","publication_year":2023,"publication_date":"2023-12-27","ids":{"openalex":"https://openalex.org/W4390263611","doi":"https://doi.org/10.1007/s10994-023-06441-7"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-023-06441-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06441-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06441-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06441-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102490364","display_name":"Zhongzhen Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Zhongzhen Wang","raw_affiliation_strings":["University College London, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017494868","display_name":"\u03a0\u03ad\u03c4\u03c1\u03bf\u03c2 \u0394\u03b5\u03bb\u03bb\u03b1\u03c0\u03cc\u03c1\u03c4\u03b1\u03c2","orcid":"https://orcid.org/0000-0002-0117-8447"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]},{"id":"https://openalex.org/I73142707","display_name":"Athens University of Economics and Business","ror":"https://ror.org/03s262162","country_code":"GR","type":"education","lineage":["https://openalex.org/I73142707"]}],"countries":["GB","GR"],"is_corresponding":false,"raw_author_name":"Petros Dellaportas","raw_affiliation_strings":["Athens University of Economics and Business, Athens, Greece","University College London, London, UK"],"raw_orcid":"https://orcid.org/0000-0002-0117-8447","affiliations":[{"raw_affiliation_string":"Athens University of Economics and Business, Athens, Greece","institution_ids":["https://openalex.org/I73142707"]},{"raw_affiliation_string":"University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010748074","display_name":"Ioannis Kosmidis","orcid":"https://orcid.org/0000-0003-1556-0302"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ioannis Kosmidis","raw_affiliation_strings":["University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102490364"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.6224,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64650059,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"113","issue":"6","first_page":"3731","last_page":"3750"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5969334840774536},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5870602130889893},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.5858725309371948},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5524250864982605},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5107696056365967},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5081193447113037},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5008678436279297},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4858062267303467},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.4809112548828125},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4802675247192383},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4653888940811157},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.4307374954223633},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.4106466472148895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4095768332481384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40321820974349976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36943793296813965},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21913886070251465}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5969334840774536},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5870602130889893},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.5858725309371948},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5524250864982605},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5107696056365967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5081193447113037},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5008678436279297},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4858062267303467},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.4809112548828125},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4802675247192383},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4653888940811157},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.4307374954223633},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.4106466472148895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4095768332481384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40321820974349976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36943793296813965},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21913886070251465},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-023-06441-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06441-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06441-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:182114","is_oa":true,"landing_page_url":null,"pdf_url":"https://wrap.warwick.ac.uk/182114/7/s10994-023-06441-7.pdf","source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1007/s10994-023-06441-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06441-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06441-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390263611.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W773845640","https://openalex.org/W1510247938","https://openalex.org/W1600310449","https://openalex.org/W1866360814","https://openalex.org/W1963826206","https://openalex.org/W1974403130","https://openalex.org/W1983125795","https://openalex.org/W1986495612","https://openalex.org/W1986706382","https://openalex.org/W1994277825","https://openalex.org/W2013912476","https://openalex.org/W2032051534","https://openalex.org/W2054619286","https://openalex.org/W2110361548","https://openalex.org/W2131628232","https://openalex.org/W2145556071","https://openalex.org/W2908056312","https://openalex.org/W6606997615","https://openalex.org/W6682569104"],"related_works":["https://openalex.org/W3087071515","https://openalex.org/W2367939674","https://openalex.org/W2999603699","https://openalex.org/W2464065341","https://openalex.org/W4283077537","https://openalex.org/W2294136611","https://openalex.org/W4328114192","https://openalex.org/W2352852554","https://openalex.org/W1484024982","https://openalex.org/W4308145032"],"abstract_inverted_index":{"Abstract":[0],"We":[1],"propose":[2],"a":[3,92,102],"flexible":[4],"nonparametric":[5],"Bayesian":[6,53,89],"modelling":[7],"framework":[8],"for":[9],"multivariate":[10],"time":[11],"series":[12],"of":[13,31,42,88,101,105,117],"count":[14],"data":[15],"based":[16,96],"on":[17,97],"tensor":[18],"factorisations.":[19],"Our":[20,108],"models":[21,48,55],"can":[22,49],"be":[23,50],"viewed":[24,51],"as":[25,52],"infinite":[26],"state":[27],"space":[28],"Markov":[29],"chains":[30],"known":[32],"maximal":[33],"order":[34],"with":[35,56,83],"non-linear":[36],"serial":[37],"dependence":[38],"through":[39],"the":[40,64,76,84,106],"introduction":[41],"appropriate":[43],"latent":[44],"variables.":[45],"Alternatively,":[46],"our":[47],"hierarchical":[54],"conditionally":[57],"independent":[58],"Poisson":[59],"distributed":[60],"observations.":[61],"Inference":[62],"about":[63],"important":[65],"lags":[66],"and":[67,115],"their":[68],"complex":[69],"interactions":[70],"is":[71,110],"achieved":[72],"via":[73,91],"MCMC.":[74],"When":[75],"observed":[77],"counts":[78],"are":[79],"large,":[80],"we":[81],"deal":[82],"resulting":[85],"computational":[86],"complexity":[87],"inference":[90],"two-step":[93],"inferential":[94],"strategy":[95],"an":[98],"initial":[99],"analysis":[100,116],"training":[103],"set":[104],"data.":[107,119],"methodology":[109],"illustrated":[111],"using":[112],"simulation":[113],"experiments":[114],"real-world":[118]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-27T08:22:11.395708","created_date":"2025-10-10T00:00:00"}