SBI -- A toolkit for simulation-based inference

• URL: http://arxiv.org/abs/2007.09114v2
• Date: Wed, 22 Jul 2020 15:43:36 GMT
• Title: SBI -- A toolkit for simulation-based inference
• Authors: Alvaro Tejero-Cantero (1), Jan Boelts (1), Michael Deistler (1), Jan-Matthis Lueckmann (1), Conor Durkan (2), Pedro J. Gon\c{c}alves (1, 3), David S. Greenberg (1, 4) and Jakob H. Macke (1, 5, 6) ((1) Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, (2) School of Informatics, University of Edinburgh, (3) Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, (4) Model-Driven Machine Learning, Centre for Materials and Coastal Research, Helmholtz-Zentrum Geesthacht, (5) Machine Learning in Science, University of T\"ubingen, (6) Empirical Inference, Max Planck Institute for Intelligent Systems, T\"ubingen)
• Abstract summary: Simulation-based inference ( SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. We present $textttsbi$, a PyTorch-based package that implements SBI algorithms based on neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging. Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. Importantly, SBI does not seek to recover a single 'best' data-compatible parameter set, but rather to identify all high probability regions of parameter space that explain observed data, and thereby to quantify parameter uncertainty. In Bayesian terminology, SBI aims to retrieve the posterior distribution over the parameters of interest. In contrast to conventional Bayesian inference, SBI is also applicable when one can run model simulations, but no formula or algorithm exists for evaluating the probability of data given parameters, i.e. the likelihood. We present $\texttt{sbi}$, a PyTorch-based package that implements SBI algorithms based on neural networks. $\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.

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