%0 Generic
%A Mertens, Ulf Kai
%C Heidelberg, Deutschland
%D 2019
%F heidok:26383
%K Deep learning; convolutional neural network; Approximate Bayesian Computation
%R 10.11588/heidok.00026383
%T Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks
%U https://archiv.ub.uni-heidelberg.de/volltextserver/26383/
%X 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.