Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation–maximization under Bayesian framework

DOI10.1080/02664763.2015.1089220OpenAlexW2201320362MaRDI QIDQ5138056

Tomasz Kozlowski, Rijan Shrestha

Publication date: 3 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2015.1089220

zbMATH Keywords

Gaussianexpectation-maximizationmaximum a posteriori

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data

Cites Work

• Unnamed Item
• Unnamed Item
• Bayesian regularization for normal mixture estimation and model-based clustering
• R. A. Fisher and the making of maximum likelihood 1912--1922
• Statistical and computational inverse problems.
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Finite mixture models
• Inverse Problem Theory and Methods for Model Parameter Estimation

This page was built for publication: Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation–maximization under Bayesian framework