BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling

Fernando Alarid Escudero, Profesor Investigador Titular de la División de Administración Pública del CIDE, Hawre Jalal y Thomas A. Trikalinos escribieron el artículo BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling en la revista Frontiers in Physiology.

 

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 program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges.

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