Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators.

ABSTRACT:

Background

Approximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set of summary statistics. While regression-based methods have been demonstrated to allow for the automatic construction of effective summary statistics, their reliance on first simulating a large training set creates a significant overhead when applying these methods to discrete stochastic models for which simulation is relatively expensive. In this τ work, we present a method to reduce this computational burden by leveraging approximate simulators of these systems, such as ordinary differential equations and τ-Leaping approximations.

SUBMITTER: Jiang RM 

PROVIDER: S-EPMC8220802 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature