Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach

DOI10.1214/19-AOAS1263zbMath1436.62661arXiv1902.03795MaRDI QIDQ2291491

Patrik Forssén, Ye Zhang, Torgny Fornstedt, Zhigang Yao

Publication date: 31 January 2020

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

Full work available at URL: https://arxiv.org/abs/1902.03795

zbMATH Keywords

integral equationvariational methodBayesianbiosensorrate constantadaptive discretization algorithm

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30)

Related Items

Stochastic asymptotical regularization for linear inverse problems ⋮ Stochastic linear regularization methods: random discrepancy principle and applications ⋮ Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach

Uses Software

• PRMLT

Cites Work

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• Goodness-of-fit tests for censored survival data
• Least angle regression. (With discussion)
• Regression trees for longitudinal and multiresponse data
• An introduction to variational methods for graphical models
• Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach
• On the ``degrees of freedom of the lasso
• Statistical inverse problems: discretization, model reduction and inverse crimes
• Mean field variational Bayes for elaborate distributions
• Hierarchical Bayesian inference for ill-posed problems via variational method
• Bayesian Calibration of Computer Models
• Inverse problems: A Bayesian perspective
• Convergence and optimality of adaptive mixed finite element methods
• Inverse problems as statistics
• Graphical Models, Exponential Families, and Variational Inference
• Mesh Refinement Processes Based on the Generalized Bisection of Simplices
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Modeling Protein Expression and Protein Signaling Pathways
• An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data
• The Bayesian estimation of common parameters from several responses