{"id":"https://openalex.org/W2586556325","doi":null,"title":"Data driven model selection and parameter estimation using semi-automatic approximate Bayesian computation to reconstruct population dynamics from ancient DNA.","display_name":"Data driven model selection and parameter estimation using semi-automatic approximate Bayesian computation to reconstruct population dynamics from ancient DNA.","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2586556325","mag":"2586556325"},"language":"en","primary_location":{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/85193","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2440/85193","pdf_url":"http://hdl.handle.net/2440/85193","source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/2440/85193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076800783","display_name":"Adam B. Rohrlach","orcid":"https://orcid.org/0000-0002-4204-5018","observed_orcids":["https://orcid.org/0000-0002-4204-5018"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Adam B. Rohrlach","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4204-5018","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5076800783"],"corresponding_institution_ids":[],"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/T10012","display_name":"Genetic diversity and population structure","score":0.6708999872207642,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/T10012","display_name":"Genetic diversity and population structure","score":0.6708999872207642,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/T10751","display_name":"Forensic and Genetic Research","score":0.1454000025987625,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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":0.13920000195503235,"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/population-size-estimation","display_name":"population size estimation","score":0.6650000214576721},{"id":"https://openalex.org/keywords/population-dynamics","display_name":"population dynamics","score":0.5640000104904175},{"id":"https://openalex.org/keywords/ancient-dna","display_name":"ancient DNA","score":0.8939999938011169},{"id":"https://openalex.org/keywords/coalescent","display_name":"coalescent","score":0.7620000243186951},{"id":"https://openalex.org/keywords/summary-statistics","display_name":"summary statistics","score":0.7229999899864197},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.6620000004768372},{"id":"https://openalex.org/keywords/bayes-factor","display_name":"Bayes factor","score":0.9020000100135803},{"id":"https://openalex.org/keywords/genetic-drift","display_name":"genetic drift","score":0.5709999799728394},{"id":"https://openalex.org/keywords/population-structure","display_name":"population structure","score":0.6399999856948853},{"id":"https://openalex.org/keywords/box-cox-transformation","display_name":"Box-Cox transformation","score":0.7480000257492065},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.3779999911785126},{"id":"https://openalex.org/keywords/migration-models","display_name":"migration models","score":0.5350000262260437},{"id":"https://openalex.org/keywords/exponential-population-growth","display_name":"exponential population growth","score":0.6610000133514404},{"id":"https://openalex.org/keywords/wright-fisher-model","display_name":"Wright-Fisher model","score":0.7099999785423279},{"id":"https://openalex.org/keywords/simulation-based-inference","display_name":"simulation-based inference","score":0.6510000228881836},{"id":"https://openalex.org/keywords/model-misspecification","display_name":"model misspecification","score":0.5339999794960022}],"concepts":[{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.8228595852851868},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6448864936828613},{"id":"https://openalex.org/C189338579","wikidata":"https://www.wikidata.org/wiki/Q290447","display_name":"Ancient DNA","level":3,"score":0.6055049896240234},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6031995415687561},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5791721343994141},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.5187966227531433},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5069863796234131},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4968319237232208},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4838615655899048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43339449167251587},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.41419917345046997},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3559615910053253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3475176692008972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3300474286079407},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16072499752044678},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1568220555782318},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/85193","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2440/85193","pdf_url":"http://hdl.handle.net/2440/85193","source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"mag:2586556325","doi":null,"is_oa":false,"landing_page_url":"https://digital.library.adelaide.edu.au/dspace/bitstream/2440/85193/8/02whole.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/85193","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2440/85193","pdf_url":"http://hdl.handle.net/2440/85193","source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2586556325.pdf","grobid_xml":"https://content.openalex.org/works/W2586556325.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2469496188","https://openalex.org/W7077860943","https://openalex.org/W2510789025","https://openalex.org/W2952308279","https://openalex.org/W2160503254","https://openalex.org/W2783431105","https://openalex.org/W7066517720","https://openalex.org/W6941032033","https://openalex.org/W2292463482","https://openalex.org/W2723035201"],"abstract_inverted_index":{"Population":[0],"genetics":[1],"is":[2,10,36,53,161,315,404,439,492],"a":[3,22,69,81,111,162,240,260,267,271,304,313,376,388,420,432,461,482,501,544,565,569,581,594],"discipline":[4],"within":[5],"the":[6,13,37,48,61,84,96,104,116,143,178,183,186,192,225,228,232,248,255,298,318,326,330,345,356,429,470,489,495,515,525,557,598,624,630],"biological":[7,179],"sciences":[8,180],"that":[9,410,507,574,586],"concerned":[11],"with":[12,231,355,401,469,588,629],"change":[14],"in":[15,21,77,83,103,125,362,424,494,577],"frequency":[16],"of":[17,19,39,50,63,71,86,98,119,149,164,185,194,207,211,227,234,242,250,263,277,294,307,337,378,397,431,460,464,510,528,530,547,571,616],"types":[18],"individuals":[20],"population":[23,54,64,131,343,379,389,617],"due":[24,181],"to":[25,47,115,152,177,182,325,386,444,453,486,524,555],"natural":[26],"selection,":[27],"mutation,":[28],"genetic":[29,51],"drift":[30,35,52],"and":[31,56,80,121,147,265,302,348,351,382,418,480,540,591,619],"gene":[32],"flow.":[33],"Genetic":[34],"part":[38],"this":[40,508,614],"process":[41,49,241,583],"explained":[42],"by":[43,597],"random":[44],"sampling.":[45],"Important":[46],"structure":[55],"so":[57,541],"we":[58,215,238,309,365,373,383,447,473,533,542],"focus":[59],"on":[60,623],"recovery":[62],"sizes":[65],"over":[66,134],"time,":[67],"given":[68,391,434],"set":[70,262,306,463],"DNA":[72,153,284],"sequences.":[73],"With":[74],"recent":[75],"advances":[76,102,123],"computational":[78],"power":[79],"growth":[82],"amount":[85],"data":[87,340,367,457,518,562],"available,":[88],"increasingly":[89],"powerful":[90],"techniques":[91,171],"are":[92,322],"being":[93],"developed":[94],"for":[95,129,142,167,188,274,283,329,368,415,478,484,514,584,606],"study":[97],"sequence":[99,154,561,572],"data.":[100,332,393],"Key":[101],"early":[105,526],"1980's":[106],"centred":[107],"around":[108],"`the":[109],"coalescent',":[110],"continuous":[112],"time":[113],"approximation":[114],"Wright-Fisher":[117],"model":[118,269,377,398,426,491,497,595,615],"reproduction,":[120],"these":[122,291],"resulted":[124],"Skyline":[126,136,199,358,633],"Plot":[127,359,634],"methods":[128,166,385],"recovering":[130],"size":[132,380],"estimates":[133],"time.":[135,578],"Plots":[137],"suffer":[138],"from":[139,204,341],"large":[140],"variances":[141],"`coalescent'":[144],"event":[145],"times,":[146],"sources":[148,206],"error":[150,229],"common":[151,280,395],"sampling":[155],"schemes.":[156],"Approximate":[157],"Bayesian":[158],"Computation":[159],"(ABC)":[160],"class":[163],"likelihood-free":[165],"statistical":[168],"inference.":[169],"ABC":[170,201,311,402],"can":[172,216,534],"trace":[173],"their":[174],"genesis":[175],"back":[176],"complexity":[184],"models":[187],"reproduction":[189],"(and":[190],"hence":[191],"intractability":[193],"likelihood":[195],"calculations).":[196],"Unfortunately,":[197],"like":[198],"Plots,":[200],"also":[202],"suffers":[203],"many":[205],"error,":[208],"not":[209,217,493,535],"least":[210],"which":[212],"occurs":[213],"when":[214,488,500],"use":[218,233,249,474,536,543],"sufficient":[219],"summary":[220,236,244,281,296,300,437,603],"statistics.":[221],"To":[222],"considerably":[223],"reduce":[224],"effect":[226],"related":[230],"insufficient":[235],"statistics,":[237,301],"explore":[239,384],"semi-automatic":[243,299,602],"statistic":[245,604],"calculation":[246,605],"through":[247],"`training":[251],"data'":[252],"(simulated":[253],"under":[254,564,593],"coalescent":[256,338,553],"model).":[257],"We":[258,289,333,408,505,559,579,611],"obtain":[259,560],"training":[261,369],"data,":[264,417,539,625],"fit":[266],"linear":[268,292,371],"(under":[270],"Box-Cox":[272],"transformation)":[273],"each":[275],"parameter":[276,320,607,621],"interest,":[278],"using":[279,303,520],"statistics":[282,297],"sequences":[285],"as":[286],"predictor":[287],"variables.":[288],"call":[290],"combinations":[293],"(insufficient)":[295],"new":[305],"simulations,":[308],"perform":[310,413,620],"where":[312,428],"simulation":[314,548,573],"retained":[316],"if":[317],"predicted":[319,327],"values":[321],"`close":[323],"enough'":[324],"parameters":[328],"observed":[331,436,456,517],"analyse":[334],"three":[335,342,516],"sets":[336,519],"simulated":[339,563],"models;":[344],"Constant,":[346],"Exponential":[347],"Migration":[349],"Models,":[350],"compare":[352],"our":[353,370,392,416,455,531,550,627],"findings":[354],"corresponding":[357,631],"analyses":[360,403],"performed":[361],"BEAST.":[363],"When":[364],"simulate":[366],"model,":[372,390,433],"must":[374],"specify":[375],"dynamics,":[381,618],"select":[387],"A":[394],"means":[396],"comparison":[399,427],"used":[400],"called":[405],"Bayes":[406,411,445],"Factors.":[407],"show":[409,506],"Factors":[412],"poorly":[414],"highlight":[419],"fundamental":[421],"bias":[422],"inherent":[423],"any":[425],"probability":[430],"an":[435,442],"statistic,":[438],"employed.":[440],"As":[441],"alternative":[443],"Factors,":[446],"apply":[448],"multiple":[449],"logistic":[450],"regression":[451],"(MLR)":[452],"classify":[454],"into":[458],"one":[459],"candidate":[462,496],"possible":[465],"models.":[466],"In":[467],"conjunction":[468],"MLR":[471,589,599],"analysis,":[472],"principal":[475],"component":[476],"analysis":[477,585],"visualisation,":[479],"introduce":[481],"method":[483,509,551],"attempting":[485],"identify":[487,613],"correct":[490],"set,":[498],"or":[499],"classification":[502,511],"seems":[503],"reasonable.":[504],"performs":[512],"well":[513],"sensitivity":[521],"analysis.":[522,635],"Due":[523],"stage":[527],"development":[529],"work,":[532],"real":[537],"world":[538],"different":[545],"type":[546,570],"since":[549],"uses":[552,601],"simulations":[554],"train":[556],"model.":[558],"`forward":[566],"simulation'":[567],"framework,":[568],"looks":[575],"forward":[576],"define":[580],"two-step":[582],"begins":[587],"classification,":[590,600],"then,":[592],"chosen":[596],"estimation":[608,622],"via":[609],"ABC.":[610],"correctly":[612],"comparing":[626],"results":[628],"BEAST":[632]},"counts_by_year":[],"updated_date":"2026-10-01T08:54:57.152042","created_date":"2017-02-17T00:00:00"}