The aim of this thesis is to propose a systematic approach for the incorporation of measurement uncertainty in the Bayesian identification of the DS of a new pharmaceutical product. Specifically, the proposed approach extends a joint Bayesian/latent variable methodology for DS identification recently proposed by Bano et al. (2018). A step-by-step methodology is proposed to handle measurement uncertainty in the calibration dataset, and three case studies are used to test its performance.
Impact of measurement error in Bayesian design space determination for pharmaceutical processes
Cattaldo, Marco
2018/2019
Abstract
The aim of this thesis is to propose a systematic approach for the incorporation of measurement uncertainty in the Bayesian identification of the DS of a new pharmaceutical product. Specifically, the proposed approach extends a joint Bayesian/latent variable methodology for DS identification recently proposed by Bano et al. (2018). A step-by-step methodology is proposed to handle measurement uncertainty in the calibration dataset, and three case studies are used to test its performance.File in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/27665