Probabilistic deep learning with variational inference

Examensarbete för masterexamen

Använd denna länk för att citera eller länka till detta dokument: https://hdl.handle.net/20.500.12380/301602
Ladda ner:
Fil Beskrivning StorlekFormat 
Master_s_Thesis_Eva_Hegnar_Probabilistic_deep_learning_with_variational_inference.pdf1.61 MBAdobe PDFThumbnail
Visa
Bibliografiska detaljer
FältVärde
Typ: Examensarbete för masterexamen
Titel: Probabilistic deep learning with variational inference
Författare: Hegnar, Eva
Sammanfattning: Deep 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.
Nyckelord: deep neural network, Bayesian inference, variational inference, black box variational inference, reparameterisation trick, probabilistic modelling, production optimisation, flow rate estimation
Utgivningsdatum: 2020
Utgivare: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
URI: https://hdl.handle.net/20.500.12380/301602
Samling:Examensarbeten för masterexamen // Master Theses



Materialet i Chalmers öppna arkiv är upphovsrättsligt skyddat och får ej användas i kommersiellt syfte!