DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
This addresses a key bottleneck in ABC inference for researchers in statistics and machine learning, though it is an incremental improvement over existing methods.
The paper tackles the challenge of selecting appropriate summary statistics for approximate Bayesian computation (ABC) in complex models with intractable likelihoods, by developing a kernel-based distribution regression framework that shows superior performance on toy and real-world problems.
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics. Although the choice of appropriate problem-specific summary statistics crucially influences the quality of the likelihood approximation and hence also the quality of the posterior sample in ABC, there are only few principled general-purpose approaches to the selection or construction of such summary statistics. In this paper, we develop a novel framework for this task using kernel-based distribution regression. We model the functional relationship between data distributions and the optimal choice (with respect to a loss function) of summary statistics using kernel-based distribution regression. We show that our approach can be implemented in a computationally and statistically efficient way using the random Fourier features framework for large-scale kernel learning. In addition to that, our framework shows superior performance when compared to related methods on toy and real-world problems.