Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits
This addresses the labor-intensive process of summary statistic selection in likelihood-free inference, offering a scalable solution for domains like systems biology, though it is incremental as it builds on existing bandit methods.
The paper tackles the challenge of selecting effective summary statistics for approximate Bayesian computation by framing it as a multi-armed bandit problem, enabling dynamic selection from a large pool without pre-processing and achieving competitive performance on a systems biology benchmark.
Approximate Bayesian computation is an established and popular method for likelihood-free inference with applications in many disciplines. The effectiveness of the method depends critically on the availability of well performing summary statistics. Summary statistic selection relies heavily on domain knowledge and carefully engineered features, and can be a laborious time consuming process. Since the method is sensitive to data dimensionality, the process of selecting summary statistics must balance the need to include informative statistics and the dimensionality of the feature vector. This paper proposes to treat the problem of dynamically selecting an appropriate summary statistic from a given pool of candidate summary statistics as a multi-armed bandit problem. This allows approximate Bayesian computation rejection sampling to dynamically focus on a distribution over well performing summary statistics as opposed to a fixed set of statistics. The proposed method is unique in that it does not require any pre-processing and is scalable to a large number of candidate statistics. This enables efficient use of a large library of possible time series summary statistics without prior feature engineering. The proposed approach is compared to state-of-the-art methods for summary statistics selection using a challenging test problem from the systems biology literature.