A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation
The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained...
Ausführliche Beschreibung
Autor*in: |
De Visscher, Lander [verfasserIn] De Baets, Bernard [verfasserIn] Baetens, Jan M. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Environmental modelling & software - Amsterdam [u.a.] : Elsevier Science, 2011, 172 |
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Übergeordnetes Werk: |
volume:172 |
DOI / URN: |
10.1016/j.envsoft.2023.105905 |
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Katalog-ID: |
ELV066530725 |
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520 | |a The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. | ||
650 | 4 | |a Approximate Bayesian Computation | |
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10.1016/j.envsoft.2023.105905 doi (DE-627)ELV066530725 (ELSEVIER)S1364-8152(23)00291-8 DE-627 ger DE-627 rda eng 690 004 VZ De Visscher, Lander verfasserin (orcid)0000-0003-3918-8648 aut A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models De Baets, Bernard verfasserin (orcid)0000-0002-3876-620X aut Baetens, Jan M. verfasserin (orcid)0000-0003-4084-9992 aut Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 172 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:172 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 172 |
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10.1016/j.envsoft.2023.105905 doi (DE-627)ELV066530725 (ELSEVIER)S1364-8152(23)00291-8 DE-627 ger DE-627 rda eng 690 004 VZ De Visscher, Lander verfasserin (orcid)0000-0003-3918-8648 aut A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models De Baets, Bernard verfasserin (orcid)0000-0002-3876-620X aut Baetens, Jan M. verfasserin (orcid)0000-0003-4084-9992 aut Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 172 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:172 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 172 |
allfields_unstemmed |
10.1016/j.envsoft.2023.105905 doi (DE-627)ELV066530725 (ELSEVIER)S1364-8152(23)00291-8 DE-627 ger DE-627 rda eng 690 004 VZ De Visscher, Lander verfasserin (orcid)0000-0003-3918-8648 aut A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models De Baets, Bernard verfasserin (orcid)0000-0002-3876-620X aut Baetens, Jan M. verfasserin (orcid)0000-0003-4084-9992 aut Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 172 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:172 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 172 |
allfieldsGer |
10.1016/j.envsoft.2023.105905 doi (DE-627)ELV066530725 (ELSEVIER)S1364-8152(23)00291-8 DE-627 ger DE-627 rda eng 690 004 VZ De Visscher, Lander verfasserin (orcid)0000-0003-3918-8648 aut A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models De Baets, Bernard verfasserin (orcid)0000-0002-3876-620X aut Baetens, Jan M. verfasserin (orcid)0000-0003-4084-9992 aut Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 172 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:172 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 172 |
allfieldsSound |
10.1016/j.envsoft.2023.105905 doi (DE-627)ELV066530725 (ELSEVIER)S1364-8152(23)00291-8 DE-627 ger DE-627 rda eng 690 004 VZ De Visscher, Lander verfasserin (orcid)0000-0003-3918-8648 aut A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models De Baets, Bernard verfasserin (orcid)0000-0002-3876-620X aut Baetens, Jan M. verfasserin (orcid)0000-0003-4084-9992 aut Enthalten in Environmental modelling & software Amsterdam [u.a.] : Elsevier Science, 2011 172 (DE-627)324486189 (DE-600)2027304-6 187-36726 nnns volume:172 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 172 |
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English |
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Enthalten in Environmental modelling & software 172 volume:172 |
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Enthalten in Environmental modelling & software 172 volume:172 |
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Article |
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findex.gbv.de |
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Approximate Bayesian Computation Inference Simulation Calibration Agent-based models Individual-based models |
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A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation |
abstract |
The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. |
abstractGer |
The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. |
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The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV066530725</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240113093221.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240113s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.envsoft.2023.105905</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV066530725</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1364-8152(23)00291-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">De Visscher, Lander</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3918-8648</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. 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