Bayesian calibration, process modeling and uncertainty quantification in biotechnology.

ABSTRACT: High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connecte

SUBMITTER: Helleckes LM 

PROVIDER: S-EPMC8939798 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature