Simultaneous identification of models and parameters of scientific simulators

• URL: http://arxiv.org/abs/2305.15174v3
• Date: Thu, 30 May 2024 14:15:22 GMT
• Title: Simultaneous identification of models and parameters of scientific simulators
• Authors: Cornelius Schröder, Jakob H. Macke,
• Abstract summary: We develop a simulation-based inference framework to identify essential model components. It can be applied to any compositional simulator without requiring evaluations. It reveals non-identifiable model components and parameters.
• Score: 7.473394133229206
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Many scientific models are composed of multiple discrete components, and scientists often make heuristic decisions about which components to include. Bayesian inference provides a mathematical framework for systematically selecting model components, but defining prior distributions over model components and developing associated inference schemes has been challenging. We approach this problem in a simulation-based inference framework: We define model priors over candidate components and, from model simulations, train neural networks to infer joint probability distributions over both model components and associated parameters. Our method, simulation-based model inference (SBMI), represents distributions over model components as a conditional mixture of multivariate binary distributions in the Grassmann formalism. SBMI can be applied to any compositional stochastic simulator without requiring likelihood evaluations. We evaluate SBMI on a simple time series model and on two scientific models from neuroscience, and show that it can discover multiple data-consistent model configurations, and that it reveals non-identifiable model components and parameters. SBMI provides a powerful tool for data-driven scientific inquiry which will allow scientists to identify essential model components and make uncertainty-informed modelling decisions.

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