Accelerating ABC methods using Gaussian processes
This method addresses computational bottlenecks in Bayesian inference for researchers using simulation-based models, though it is incremental as it builds on existing ABC and GP techniques.
The paper tackles the computational expense of Approximate Bayesian Computation (ABC) by introducing Gaussian process (GP) acceleration, which reduces the number of simulations needed; for the Ricker model, it achieves accurate posterior approximations with 100 times fewer evaluations than Monte Carlo methods.
Approximate Bayesian computation (ABC) methods are used to approximate posterior distributions using simulation rather than likelihood calculations. We introduce Gaussian process (GP) accelerated ABC, which we show can significantly reduce the number of simulations required. As computational resource is usually the main determinant of accuracy in ABC, GP-accelerated methods can thus enable more accurate inference in some models. GP models of the unknown log-likelihood function are used to exploit continuity and smoothness, reducing the required computation. We use a sequence of models that increase in accuracy, using intermediate models to rule out regions of the parameter space as implausible. The methods will not be suitable for all problems, but when they can be used, can result in significant computational savings. For the Ricker model, we are able to achieve accurate approximations to the posterior distribution using a factor of 100 fewer simulator evaluations than comparable Monte Carlo approaches, and for a population genetics model we are able to approximate the exact posterior for the first time.