BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling

BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are many barriers to using Bayesian calibration in health decision sciences stemming from the need to progr...

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Bibliographic Details
Main Authors: Hawre Jalal, Thomas A. Trikalinos, Fernando Alarid-Escudero
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-05-01
Series:Frontiers in Physiology
Subjects:
Bayesian calibration
machine learning
mechanistic models
artificial neural networks
emulators
surrogate models
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2021.662314/full

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