🤖 AI Summary
This work addresses the challenge of efficiently calibrating unknown parameters in computationally expensive stochastic simulation models while optimally allocating resources between exploring new input configurations and replicating simulations. To this end, the authors propose a Bayesian active learning framework tailored for posterior density estimation. The approach introduces an uncertainty-aware acquisition criterion and derives two distinct acquisition functions—one for exploration and one for replication—dynamically balancing these strategies to enhance surrogate model construction. Empirical evaluations on both synthetic benchmarks and a real-world epidemiological model demonstrate that the proposed method significantly improves the efficiency of learning the parameter posterior distribution, achieving comparable or superior accuracy with substantially fewer simulation runs.
📝 Abstract
Simulation-based calibration aims to infer unknown parameters of complex simulation models by aligning model outputs with real-world observations. When simulation runs are computationally expensive, statistical emulators trained on simulation data are used to efficiently approximate the model. An intelligent, adaptive selection of simulation inputs for building the emulator can substantially improve the efficiency of the calibration process. This task is particularly challenging for stochastic simulations with noisy outputs, since both selecting new input locations (exploration) and allocating repeated runs at existing inputs (replication) are essential for efficiently learning the input-output relationship. In this paper, we introduce an active learning framework that adaptively balances exploration and replication for data-efficient calibration. Our uncertainty-aware acquisition criterion targets learning the posterior density of the unknown simulation parameters, and we derive two corresponding forms of the acquisition function for exploration and replication. Building on these, we propose a strategy that, at each stage of the sequential design, chooses between exploration and replication to most effectively reduce the uncertainty in the estimate of the posterior density of the simulation parameters. Experiments on synthetic benchmarks and a real epidemiological model demonstrate that our approach significantly improves learning of the posterior distribution of the simulation parameters while reducing the number of required simulations, making it well-suited for expensive stochastic simulation settings.