An interpolation approach for fitting computationally intensive models
Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execut...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Richard Moore, L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia - Billings, Wade ELSEVIER, 2021, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:29 ; year:2014 ; pages:53-65 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.cogsys.2013.09.001 |
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| 520 | |a Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. | ||
| 520 | |a Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. | ||
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10.1016/j.cogsys.2013.09.001 doi GBVA2014003000007.pica (DE-627)ELV017292468 (ELSEVIER)S1389-0417(13)00050-8 DE-627 ger DE-627 rakwb eng 150 150 DE-600 610 VZ 44.87 bkl Richard Moore, L. verfasserin aut An interpolation approach for fitting computationally intensive models 2014transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Cognitive moderator Elsevier Model proxy Elsevier Cognitive model Elsevier Mathematical model Elsevier Gunzelmann, Glenn oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:29 year:2014 pages:53-65 extent:13 https://doi.org/10.1016/j.cogsys.2013.09.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 29 2014 53-65 13 045F 150 |
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10.1016/j.cogsys.2013.09.001 doi GBVA2014003000007.pica (DE-627)ELV017292468 (ELSEVIER)S1389-0417(13)00050-8 DE-627 ger DE-627 rakwb eng 150 150 DE-600 610 VZ 44.87 bkl Richard Moore, L. verfasserin aut An interpolation approach for fitting computationally intensive models 2014transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Cognitive moderator Elsevier Model proxy Elsevier Cognitive model Elsevier Mathematical model Elsevier Gunzelmann, Glenn oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:29 year:2014 pages:53-65 extent:13 https://doi.org/10.1016/j.cogsys.2013.09.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 29 2014 53-65 13 045F 150 |
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10.1016/j.cogsys.2013.09.001 doi GBVA2014003000007.pica (DE-627)ELV017292468 (ELSEVIER)S1389-0417(13)00050-8 DE-627 ger DE-627 rakwb eng 150 150 DE-600 610 VZ 44.87 bkl Richard Moore, L. verfasserin aut An interpolation approach for fitting computationally intensive models 2014transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Cognitive moderator Elsevier Model proxy Elsevier Cognitive model Elsevier Mathematical model Elsevier Gunzelmann, Glenn oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:29 year:2014 pages:53-65 extent:13 https://doi.org/10.1016/j.cogsys.2013.09.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 29 2014 53-65 13 045F 150 |
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10.1016/j.cogsys.2013.09.001 doi GBVA2014003000007.pica (DE-627)ELV017292468 (ELSEVIER)S1389-0417(13)00050-8 DE-627 ger DE-627 rakwb eng 150 150 DE-600 610 VZ 44.87 bkl Richard Moore, L. verfasserin aut An interpolation approach for fitting computationally intensive models 2014transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. Cognitive moderator Elsevier Model proxy Elsevier Cognitive model Elsevier Mathematical model Elsevier Gunzelmann, Glenn oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:29 year:2014 pages:53-65 extent:13 https://doi.org/10.1016/j.cogsys.2013.09.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 29 2014 53-65 13 045F 150 |
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An interpolation approach for fitting computationally intensive models |
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| title_full |
An interpolation approach for fitting computationally intensive models |
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Richard Moore, L. |
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The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia |
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The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia |
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eng |
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2014 |
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Richard Moore, L. |
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Elektronische Aufsätze |
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Richard Moore, L. |
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10.1016/j.cogsys.2013.09.001 |
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150 610 |
| title_sort |
an interpolation approach for fitting computationally intensive models |
| title_auth |
An interpolation approach for fitting computationally intensive models |
| abstract |
Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. |
| abstractGer |
Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. |
| abstract_unstemmed |
Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. |
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| title_short |
An interpolation approach for fitting computationally intensive models |
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https://doi.org/10.1016/j.cogsys.2013.09.001 |
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Gunzelmann, Glenn |
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Gunzelmann, Glenn |
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10.1016/j.cogsys.2013.09.001 |
| up_date |
2024-07-06T21:37:37.973Z |
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