TY  - GEN
ID  - heidok26383
KW  - Deep learning; convolutional neural network; Approximate Bayesian Computation
AV  - public
CY  - Heidelberg, Deutschland
TI  - Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks
Y1  - 2019///
N2  - 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.
A1  - Mertens, Ulf Kai
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/26383/
ER  -