Mertens, Ulf Kai
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Abstract
In this thesis, a fast and likelihood-free approach for parameter inference is introduced. The convolutional neural network, named DeepInference, learns to predict the posterior mean and variance of multi-dimensional posterior distributions from raw simulated data. It is shown how DeepInference can be applied to the drift diffusion model (DDM) and the Lévy flight model, a likelihood-free extension of the DDM. For both models, state-of-the-art results in terms of accuracy of parameter estimation are observed.
Document type: | Dissertation |
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Supervisor: | Voß, Prof. Dr. Andreas |
Place of Publication: | Heidelberg, Deutschland |
Date of thesis defense: | 11 April 2019 |
Date Deposited: | 10 May 2019 11:46 |
Date: | 2019 |
Faculties / Institutes: | The Faculty of Behavioural and Cultural Studies > Institute of Psychology |
DDC-classification: | 004 Data processing Computer science 150 Psychology 310 General statistics |
Controlled Keywords: | Likelihood, Maschinelles Lernen, Neuronales Netz |
Uncontrolled Keywords: | Deep learning; convolutional neural network; Approximate Bayesian Computation |