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Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks

Mertens, Ulf Kai

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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.

Item Type: Dissertation
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
Subjects: 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
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