Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach

DOI10.3934/mbe.2021335zbMath1501.92038OpenAlexW3197788060MaRDI QIDQ2092109

Susan E. Luczak, I. G. Rosen, Keenan Hawekotte

Publication date: 2 November 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021335

zbMATH Keywords

distributed parameter systemsdeconvolutionBayesian estimationGalerkin methodsposterior consistencylinear semigroup theoryblood alcohol concentrationbreath alcohol concentrationtransdermal alcohol concentration

Mathematics Subject Classification ID

Biosensors (not for medical applications) (92C47)

Related Items (2)

Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* ⋮ A population model‐based linear‐quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors
• Semigroups of linear operators and applications to partial differential equations
• Fréchet differentiability of parameter-dependent analytic semigroups
• Blind deconvolution for distributed parameter systems with unbounded input and output and determining blood alcohol concentration from transdermal biosensor data
• New approaches to Bayesian consistency
• Estimation techniques for distributed parameter systems
• Optimality of adaptive Galerkin methods for random parabolic partial differential equations
• Deconvolving an estimate of breath measured blood alcohol concentration from biosensor collected transdermal ethanol data
• Inverse problems: A Bayesian perspective
• Sparse tensor discretizations of high-dimensional parametric and stochastic PDEs
• Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh
• Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data
• Bayesian Estimation of the Spectral Density of a Time Series
• A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

This page was built for publication: Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach