# SMB 2021 minisymposium

I have presented my work with Ruth Baker on multifidelity ABC in the Stochastic Systems Biology: Theory and Simulation minisymposium at SMB 2021.

**Learning a multifidelity simulation strategy for likelihood-free Bayesian inference.**

*Abstract*:
Likelihood-free Bayesian inference is a popular approach to calibrating
complex mathematical models typical of biological systems, where
likelihoods are often intractable. However, being reliant on repeated
model simulation, the complexity that prohibits the likelihood
calculation can also cause these methods to suffer from a significant
computational burden. Multifidelity inference methods have been shown to
reduce this burden by exploiting approximate simulations, such as
coarser numerics or lower-dimensional models. By incorporating both
high- and low-fidelity simulations, computational savings can be
achieved without introducing any further bias in the resulting
likelihood-free posterior. Instead, these approaches are forced to trade
between reducing computational burden and increasing estimator variance.
This trade-off is balanced by optimally assigning a simulation budget
between the models at different fidelities. We will discuss how the
optimal multifidelity simulation strategy can be learned in parallel
with the posterior, and the multifidelity algorithm thus adaptively
tuned as the posterior is uncovered.