Buchumschlag

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Voß, Andreas (VerfasserIn) , Mertens, Ulf K. (VerfasserIn) , Radev, Stefan (VerfasserIn)
Dokumenttyp: Datenbank Forschungsdaten
Sprache:Englisch
Veröffentlicht: Heidelberg Universität 2018-06-22
DOI:10.11588/data/HY4OBJ
Schlagworte:
Forschungsdaten
Datenbank
Online-Zugang:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.11588/data/HY4OBJ
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/HY4OBJ
Volltext
Verfasserangaben:Andreas Voss, Ulf K. Mertens, Stefan T. Radev
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 02.07.2018
Deposit date: 2018-06-21
Grant information: Deutsche Forschungsgemeinschaft (DFG): Vo-1288-2
Beschreibung:Online Resource
DOI:10.11588/data/HY4OBJ

Ähnliche Einträge