Probabilistic deep learning with variational inference

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/301602
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dc.contributor.authorHegnar, Eva-
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.date.accessioned2020-09-01T09:35:42Z-
dc.date.available2020-09-01T09:35:42Z-
dc.date.issued2020sv
dc.date.submitted2020-
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301602-
dc.description.abstractDeep neural networks are used in the petroleum industry to model gas and oil rate. To optimise the production, the uncertainty of the network predictions is desirable. The neural network weights are equipped with prior distributions to be able to quantify the uncertainty of the model predictions within the Bayesian paradigm. To obtain a numerically feasible procedure two different approaches of variational inference are used and compared; black box variational inference and variational inference using the reparameterisation trick. Both approaches are applied to real measurements of gas and oil rate, which were given by Solution Seeker, a company providing production optimisation to the petroleum industry. The results show a more stable convergence using the reparameterisation trick. The uncertainty in predictions is possible to be quantified using variational inference but setting a proper prior distribution is difficult.sv
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths-
dc.subjectdeep neural network, Bayesian inference, variational inference, black box variational inference, reparameterisation trick, probabilistic modelling, production optimisation, flow rate estimationsv
dc.titleProbabilistic deep learning with variational inferencesv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH-
dc.contributor.examinerPicchini, Umberto-
dc.contributor.supervisorSchauer, Moritz-
dc.identifier.coursecodeMVEX03sv
Collection:Examensarbeten för masterexamen // Master Theses



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