Efficient acquisition rules for model-based approximate Bayesian computation

Understand

Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible.

• However, many ABC algorithms require a large number of simulations, which can be costly.
• To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed.
• Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution.