Efficient Bayesian inference for mechanistic modelling with high-throughput data.

ABSTRACT: Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed

SUBMITTER: Martina Perez S 

PROVIDER: S-EPMC9249175 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature