Uncertainty Quantification in Chromatography Process Identification Based on Markov Chain Monte Carlo

DOI10.1007/978-3-319-38869-4_6zbMath1347.65006OpenAlexW2498543085MaRDI QIDQ2822760

Antônio José da Silva Neto, Diego Campos-Knupp, Mirtha Irizar Mesa, Leôncio D. Tavares Câmara

Publication date: 4 October 2016

Published in: Mathematical Modeling and Computational Intelligence in Engineering Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-38869-4_6

zbMATH Keywords

uncertaintyparameter estimationMarkov chain Monte CarlochromatographyBayesian techniquesconvergence assessing methodsdelayed rejection adaptive Metropolis algorithm

Mathematics Subject Classification ID

Monte Carlo methods (65C05)

Uses Software

• MCMC
• BayesDA

Cites Work

• Unnamed Item
• Optimal scaling for various Metropolis-Hastings algorithms.
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• An adaptive Metropolis algorithm

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