Improving approximate Bayesian computation via quasi-Monte Carlo

Understand

ABC (approximate Bayesian computation) is a general approach for dealing with models with an intractable likelihood.

• In this work, we derive ABC algorithms based on QMC (quasi- Monte Carlo) sequences.
• We show that the resulting ABC estimates have a lower variance than their Monte Carlo counter-parts.
• We also develop QMC variants of sequential ABC algorithms, which progressively adapt the proposal distribution and the acceptance threshold.