sbi reloaded: a toolkit for simulation-based inference workflows

TL;DR

The paper introduces sbi reloaded, a comprehensive PyTorch toolkit that facilitates simulation-based Bayesian inference for complex models without requiring likelihood evaluations or gradients, enabling flexible and scalable parameter estimation.

Contribution

It presents an extended, flexible, and user-friendly toolkit for SBI that supports various inference methods, neural architectures, and diagnostics, enhancing accessibility for scientists and engineers.

Findings

Supports a wide range of SBI algorithms and neural network architectures.

Enables parallelized, likelihood-free inference without model gradients.

Provides default settings and customization for diverse workflows.

Abstract

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.