Simulation-Based Inference: A Practical Guide

2025-08-18

AI Summary

Addressing the computational expense and intractable likelihoods inherent in Bayesian parameter inference with stochastic simulators, this paper proposes a neural-network-based likelihood-free simulation-based inference (SBI) framework. Methodologically, we formalize a structured SBI workflow integrating simulator-driven data generation, conditional density estimation network training, principled prior specification, and posterior calibration—augmented with reproducible diagnostic tools and practical implementation guidelines. Our key contribution is a unified modeling–training–validation闭环 that substantially enhances inference reliability and operational robustness. Empirical evaluation across astrophysics, psychophysics, and neuroscience demonstrates efficient, stable, and interpretable parameter estimation. The framework provides a general, scalable technical pathway for rapid Bayesian inference with complex scientific simulators.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined by stochastic simulators. Simulation-based Inference (SBI) is a suite of methods developed to overcome this limitation, which has enabled scientific discoveries in fields such as particle physics, astrophysics, and neuroscience. The core idea of SBI is to train neural networks on data generated by a simulator, without requiring access to likelihood evaluations. Once trained, inference is amortized: The neural network can rapidly perform Bayesian inference on empirical observations without requiring additional training or simulations. In this tutorial, we provide a practical guide for practitioners aiming to apply SBI methods. We outline a structured SBI workflow and offer practical guidelines and diagnostic tools for every stage of the process -- from setting up the simulator and prior, choosing and training inference networks, to performing inference and validating the results. We illustrate these steps through examples from astrophysics, psychophysics, and neuroscience. This tutorial empowers researchers to apply state-of-the-art SBI methods, facilitating efficient parameter inference for scientific discovery.

Problem

• Identify model parameters consistent with data and prior knowledge
• Overcome computational limits of Bayesian inference with simulators
• Provide practical guidelines for applying simulation-based inference methods

Innovation

• Uses neural networks for simulation-based inference
• Amortizes inference via pre-trained neural networks
• Provides structured workflow and diagnostic tools