Probabilistic deep learning with variational inference

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dc.contributor.authorHegnar, Eva
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerPicchini, Umberto
dc.contributor.supervisorSchauer, Moritz
dc.date.accessioned2020-09-01T09:35:42Z
dc.date.available2020-09-01T09:35:42Z
dc.date.issued2020sv
dc.date.submitted2020
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.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301602
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
local.programmeEngineering mathematics and computational science (MPENM), MSc
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