{"id":"https://openalex.org/W7223263221","doi":"https://doi.org/10.48550/arxiv.2610.30291","title":"Generalizing Metropolis-Hastings for DAG Sampling","display_name":"Generalizing Metropolis-Hastings for DAG Sampling","publication_year":2026,"publication_date":"2026-10-21","ids":{"openalex":"https://openalex.org/W7223263221","doi":"https://doi.org/10.48550/arxiv.2610.30291"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.30291","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.30291","pdf_url":"https://arxiv.org/pdf/2610.30291","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.30291","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5144978464","display_name":"Daniele Nikzad","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Nikzad, Daniele","raw_affiliation_strings":["Department of Computer Science University of Helsinki Helsinki , FI-00014 , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Helsinki Helsinki , FI-00014 , Finland","institution_ids":["https://openalex.org/I133731052"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088812539","display_name":"Mikko Koivisto","orcid":"https://orcid.org/0000-0001-9662-3605","observed_orcids":["https://orcid.org/0000-0001-9662-3605"]},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Koivisto, Mikko","raw_affiliation_strings":["Department of Computer Science University of Helsinki Helsinki , FI-00014 , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Helsinki Helsinki , FI-00014 , Finland","institution_ids":["https://openalex.org/I133731052"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133731052"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.6079000234603882,"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"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.6079000234603882,"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"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.38960000872612,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":9.999999747378752e-05,"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/metropolis-hastings-algorithm","display_name":"Metropolis-Hastings algorithm","score":0.8080000281333923},{"id":"https://openalex.org/keywords/directed-acyclic-graphs","display_name":"directed acyclic graphs","score":0.8339999914169312},{"id":"https://openalex.org/keywords/bayesian-network-structure-learning","display_name":"Bayesian network structure learning","score":0.9919999837875366},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.859000027179718},{"id":"https://openalex.org/keywords/proposal-distribution","display_name":"proposal distribution","score":0.7570000290870667},{"id":"https://openalex.org/keywords/acceptance-criteria","display_name":"acceptance criteria","score":0.7379999756813049},{"id":"https://openalex.org/keywords/posterior-approximation","display_name":"posterior approximation","score":0.44200000166893005}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6639000177383423},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6349999904632568},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6334999799728394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5559999942779541},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4756999909877777},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.46299999952316284},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4323999881744385},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42660000920295715},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.42170000076293945},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4065999984741211},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.38199999928474426},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.33709999918937683},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.32019999623298645},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.28130000829696655},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.2612000107765198}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.30291","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.30291","pdf_url":"https://arxiv.org/pdf/2610.30291","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.30291","doi":"https://doi.org/10.48550/arxiv.2610.30291","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.30291","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.30291","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.30291","pdf_url":"https://arxiv.org/pdf/2610.30291","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223263221.pdf","grobid_xml":"https://content.openalex.org/works/W7223263221.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7102701229","https://openalex.org/W7197490194","https://openalex.org/W4388276162","https://openalex.org/W7198015363","https://openalex.org/W2408281903","https://openalex.org/W4387560272","https://openalex.org/W2267567366","https://openalex.org/W3002588292","https://openalex.org/W7064456021","https://openalex.org/W1721345319"],"abstract_inverted_index":{"A":[0],"fundamental":[1],"challenge":[2],"in":[3,9,149],"Bayesian":[4],"network":[5],"structure":[6],"learning":[7],"lies":[8],"efficiently":[10,129],"approximating":[11],"the":[12,24,28,48,54,57,69,92,99,102,152,156,173],"posterior":[13],"distribution":[14],"of":[15,30,53,59,68,91,108,155,165],"directed":[16],"acyclic":[17],"graphs":[18],"(DAGs).":[19],"For":[20],"large,":[21],"high-dimensional":[22],"datasets,":[23],"task":[25],"remains":[26],"beyond":[27],"capabilities":[29],"current":[31,103],"methods.":[32],"This":[33,167],"work":[34],"advances":[35],"Markov":[36,93,124],"chain":[37,94],"Monte":[38],"Carlo":[39],"algorithms":[40],"by":[41],"introducing":[42],"novel":[43],"moves":[44,61],"that":[45,72,126,142],"simultaneously":[46],"resample":[47],"parents":[49],"for":[50,86,97],"multiple":[51],"nodes":[52],"DAG.":[55],"At":[56],"core":[58],"these":[60],"is":[62,84],"duet":[63,177],"sampling,":[64],"a":[65,78,88,161],"new":[66,144],"generalization":[67],"Metropolis-Hastings":[70,133],"algorithm":[71],"selects":[73],"two":[74],"proposal":[75,116],"distributions":[76,117],"from":[77],"pool":[79,164],"at":[80,135],"each":[81],"iteration.":[82],"One":[83],"used":[85],"generating":[87],"candidate":[89],"state":[90,154],"and":[95,121,148],"another":[96],"evaluating":[98],"return":[100],"to":[101,179],"state;":[104],"both":[105,146],"are":[106],"ingredients":[107],"an":[109],"appropriately":[110],"crafted":[111],"acceptance":[112],"criterion.":[113],"Pairing":[114],"different":[115],"provides":[118],"additional":[119,137],"freedom":[120],"enables":[122],"constructing":[123],"chains":[125],"mix":[127],"more":[128,162],"than":[130],"their":[131,181],"basic":[132],"counterparts,":[134],"no":[136],"computational":[138],"cost.":[139],"Experiments":[140],"show":[141],"our":[143],"moves,":[145],"individually":[147],"combination,":[150],"outperform":[151],"previous":[153,174],"art,":[157],"which":[158],"relies":[159],"on":[160],"restrictive":[163],"moves.":[166],"advantage":[168],"persists":[169],"even":[170],"when":[171],"letting":[172],"methods":[175],"leverage":[176],"sampling":[178],"enhance":[180],"performance.":[182]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-24T00:00:00"}
