sbi reloaded: a toolkit for simulation-based inference workflows (2411.17337v1)

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 $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ 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 $\texttt{sbi}$ 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.

• The paper introduces an advanced toolkit that leverages neural networks for efficient, likelihood-free Bayesian inference on complex simulators.
• It implements diverse neural architectures and sampling strategies to optimize both amortized and sequential simulation workflows.
• User-friendly interfaces and robust diagnostic tools promote broad adoption in research areas such as neuroscience, physics, and engineering.

An Overview of the "sbi reloaded" Toolkit for Simulation-Based Inference

The paper "sbi reloaded: a toolkit for simulation-based inference workflows," presents a refined and comprehensive toolkit specifically designed for simulation-based inference (SBI) applications. The sbi suite leverages neural network-based methodologies to perform Bayesian inference on simulated data, circumventing the need for likelihood evaluations and gradient calculations, thus broadening the applicability of Bayesian techniques to complex, black-box simulators.

Key Features and Innovations

The "sbi reloaded" toolkit stands out due to its extensive feature set and flexibility, making it a powerful tool for both researchers and applied scientists. The toolkit is constructed on a PyTorch foundation, allowing it to harness the robust functionalities associated with neural networks. The methodological innovations include:

1. Neural Network-Based SBI Algorithms: Techniques such as Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE) enable substantial parallelization of simulations. This is pivotal in maximizing the computational efficiency of SBI processes. The toolkit supports both amortized and sequential learning modes, allowing users to tailor the simulation strategy to their specific needs.
2. Diverse Neural Network Architectures: A range of architectures such as normalizing flows, diffusion models, and flow matching are supported. The compatibility with nflows and Zuko libraries underlines the toolkit's capacity for flexibility and extensibility in terms of problem-solving and application domains.
3. Comprehensive Sampling and Diagnostic Tools: "sbi reloaded" employs a variety of sampling strategies encompassing MCMC, variational inference, and rejection sampling. Additionally, sophisticated diagnostic tools like simulation-based calibration (SBC) and local C2ST provide mechanisms to evaluate and calibrate model inference, crucial for ensuring the reliability of the results.
4. User-Friendly Interfaces: Recognizing the diverse audience ranging from SBI researchers to domain-specific practitioners, the toolkit offers both high-level and low-level APIs, allowing complete control over simulations, training, and sampling procedures. This versatility extends its usability across different research contexts and encourages broader adoption.

Practical and Theoretical Implications

The sbi toolkit embodies significant practical implications for enhancing the effectiveness of simulation-based studies across various scientific fields, including neuroscience, physics, and engineering. By abstracting the complexities inherent in Bayesian inference through sophisticated neural networks and user-friendly interfaces, it facilitates broader application in real-world, computationally intensive scenarios.

Theoretically, sbi propels the understanding and development of Bayesian inference methodologies. As the toolkit supports non-gradient-based inference on non-differentiable simulators, it gives rise to potential new research avenues focusing on alternative SBI techniques, possibly leading to novel algorithms that further push the boundaries of computational efficiency and scalability.

Future Developments

Moving forward, the continuous evolution of the sbi package is anticipated to integrate additional neural network architectures and refine the existing ones. Future iterations could explore enhanced integration with peer inference frameworks and reinforce its open-source collaborative ethos. Such improvements could lead to a richer feature set, improving both the performance and accessibility of SBI methodologies.

In conclusion, "sbi reloaded" provides a comprehensive setting for conducting state-of-the-art simulation-based inference, combining the strengths of neural network technologies with practical Bayesian inference strategies. Its extensive feature set, supported by a thriving user community, ensures that it remains an indispensable tool for advancing research across diverse scientific disciplines.