Efficient Bayesian inference for stochastic agent-based models.

ABSTRACT: The modelling of many real-world problems relies on computationally heavy simulations of randomly interacting individuals or agents. However, the values of the parameters that underlie the interactions between agents are typically poorly known, and hence they need to be inferred from macroscopic observations of the system. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue in a Bayesian setting through the use of machine learning methods: One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumvent the need for Bayesian sam

SUBMITTER: Jorgensen ACS 

PROVIDER: S-EPMC9576090 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature