title: Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks
creator: Mertens, Ulf Kai
subject: ddc-004
subject: 004 Data processing Computer science
subject: ddc-150
subject: 150 Psychology
subject: ddc-310
subject: 310 General statistics
description: 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.
date: 2019
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/26383/1/Dissertation_A_Ulf_Mertens.pdf
identifier: DOI:10.11588/heidok.00026383
identifier: urn:nbn:de:bsz:16-heidok-263839
identifier:   Mertens, Ulf Kai  (2019) Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/26383/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng