Numerical Methods for Bayesian Inference in Hilbert Spaces

Numerical Methods for Bayesian Inference in Hilbert Spaces

Bayesian inference occurs when prior knowledge about uncertain parameters in mathematical models is merged with new observational data related to the model outcome. In this thesis we focus on models given by partial differential equations where the uncertain parameters are coefficient functions belo...

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Bibliographic Details
Main Author: Sprungk, Björn
Other Authors: TU Chemnitz, Fakultät für Mathematik
Format: Doctoral Thesis
Language:English
Published: Universitätsbibliothek Chemnitz 2018
Subjects:
Bayessche Inferenz
Unsicherheitsquantifizierung
zufällige partielle Differentialgleichungen
Ensemble Kalmanfilter
Markowketten-Monte-Carlo
Metropolis-Hastings-Algorithmus
Grundwassersimulation
Bayesian inference
uncertainty quantification
random partial differential equations
ensemble Kalman filter
Markov chain Monte Carlo
Metropolis-Hastings algorithm
spectral gaps
groundwater flow simulation
ddc:518
ddc:519
Spektrallücke
Differentialgleichung
Quantifizierung
Online Access:http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-226748
http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-226748
http://www.qucosa.de/fileadmin/data/qucosa/documents/22674/Dissertation_Sprungk.pdf
http://www.qucosa.de/fileadmin/data/qucosa/documents/22674/signatur.txt.asc

Internet

http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-226748
http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-226748
http://www.qucosa.de/fileadmin/data/qucosa/documents/22674/Dissertation_Sprungk.pdf
http://www.qucosa.de/fileadmin/data/qucosa/documents/22674/signatur.txt.asc

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