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|a In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to be trained with simulated data to learn the relation of parameters and data. Then, the trained network can be used to re-estimate parameters for real data. The efficiency and robustness of this approach was tested for two decision models based on continuous evidence accumulation. Study 1 investigated the recovery of parameters of the diffusion model, and Study 2 addressed the same question for a Lévy-Flight model. Results demonstrate that the machine-learning approach is superior to traditional multidimensional search algorithms that maximize the likelihood, both in terms of correlations of estimated parameters with true parameters and with regard to absolute deviations. The new approach also excels the maximum likelihood based search pertaining the robustness in the presence of contaminated data.
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In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to be trained with simulated data to learn the relation of parameters and data. Then, the trained network can be used to re-estimate parameters for real data. The efficiency and robustness of this approach was tested for two decision models based on continuous evidence accumulation. Study 1 investigated the recovery of parameters of the diffusion model, and Study 2 addressed the same question for a Lévy-Flight model. Results demonstrate that the machine-learning approach is superior to traditional multidimensional search algorithms that maximize the likelihood, both in terms of correlations of estimated parameters with true parameters and with regard to absolute deviations. The new approach also excels the maximum likelihood based search pertaining the robustness in the presence of contaminated data. |
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Gesehen am 02.07.2018, Deposit date: 2018-06-21, Grant information: Deutsche Forschungsgemeinschaft (DFG): Vo-1288-2 |
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Voß, Andreas 1972- VerfasserIn (DE-588)1028372574 (DE-627)730618919 (DE-576)375905065 aut, Learning the likelihood using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] Andreas Voss, Ulf K. Mertens, Stefan T. Radev, Heidelberg Universität 2018-06-22, 1 Online-Ressource (5 Files), Computerdaten cod rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Gesehen am 02.07.2018, Deposit date: 2018-06-21, Grant information: Deutsche Forschungsgemeinschaft (DFG): Vo-1288-2, In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to be trained with simulated data to learn the relation of parameters and data. Then, the trained network can be used to re-estimate parameters for real data. The efficiency and robustness of this approach was tested for two decision models based on continuous evidence accumulation. Study 1 investigated the recovery of parameters of the diffusion model, and Study 2 addressed the same question for a Lévy-Flight model. Results demonstrate that the machine-learning approach is superior to traditional multidimensional search algorithms that maximize the likelihood, both in terms of correlations of estimated parameters with true parameters and with regard to absolute deviations. The new approach also excels the maximum likelihood based search pertaining the robustness in the presence of contaminated data., Forschungsdaten (DE-588)1098579690 (DE-627)857755366 (DE-576)469182156 gnd-content, Datenbank (DE-588)4011119-2 (DE-627)106354256 (DE-576)208891943 gnd-content, Mertens, Ulf K. 1989- VerfasserIn (DE-588)1147723338 (DE-627)1006745602 (DE-576)414122860 aut, Radev, Stefan 1993- VerfasserIn (DE-588)1155312392 (DE-627)1016724993 (DE-576)501536248 aut, http://dx.doi.org/10.11588/data/HY4OBJ Verlag Resolving-System kostenfrei Volltext, https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/HY4OBJ Verlag kostenfrei Volltext, http://dx.doi.org/10.11588/data/HY4OBJ LFER, LFER 2018-07-10T00:00:00Z |
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Voß, Andreas, Mertens, Ulf K., Radev, Stefan, Learning the likelihood: using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset], In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to be trained with simulated data to learn the relation of parameters and data. Then, the trained network can be used to re-estimate parameters for real data. The efficiency and robustness of this approach was tested for two decision models based on continuous evidence accumulation. Study 1 investigated the recovery of parameters of the diffusion model, and Study 2 addressed the same question for a Lévy-Flight model. Results demonstrate that the machine-learning approach is superior to traditional multidimensional search algorithms that maximize the likelihood, both in terms of correlations of estimated parameters with true parameters and with regard to absolute deviations. The new approach also excels the maximum likelihood based search pertaining the robustness in the presence of contaminated data., Forschungsdaten, Datenbank |
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507149998 |
| title |
Learning the likelihood: using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] |
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Learning the likelihood using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] |
| title_full |
Learning the likelihood using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] Andreas Voss, Ulf K. Mertens, Stefan T. Radev |
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Learning the likelihood using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] Andreas Voss, Ulf K. Mertens, Stefan T. Radev |
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Learning the likelihood using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] Andreas Voss, Ulf K. Mertens, Stefan T. Radev |
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Learning the likelihood |
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learning the likelihood using deepinference for the estimation of diffusion model and levy flight parameters dataset |
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using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset] |
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Forschungsdaten, Datenbank |
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