sbi reloaded: a toolkit for simulation-based inference workflows
This toolkit addresses a significant problem for scientists and engineers working with simulators by providing a flexible and efficient solution for parameter inference, though it is incremental as it builds on existing SBI methods.
The paper tackles the challenge of tuning simulator parameters to match observed data by introducing sbi, a PyTorch-based toolkit for simulation-based inference workflows that enables Bayesian inference without requiring likelihood evaluations or gradients, allowing scientists and engineers to apply state-of-the-art methods to black-box simulators.
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings, but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.