arXiv Machine Learning By Maximilian Dax, Theo Heimel, Gilles Louppe

An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 21702v1 Announce Type: new Abstract: Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and the inversion of detector effects.

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arXiv Machine Learning
Jul 27

Simulation-Based Empirical Bayes

arXiv:2607. 21843v1 Announce Type: cross Abstract: Empirical Bayes (EB) performs simultaneous inference across many related latent variables.

By Xinwei Shen, Diana Cai, Cheng Zhang, David M. Blei
arXiv Machine Learning
5d ago

Mixed neural posterior estimation for simulators with discrete and continuous parameters

The paper extends Neural Posterior Estimation (NPE) to handle simulators whose parameter spaces contain both discrete and continuous dimensions. It introduces an inference network that factorizes the joint posterior into discrete and continuous components, using an autoregressive classifier for the discrete part and a generative model for the continuous part, trained jointly with a single simulation-based objective. A diagnostic tool for assessing calibration of the mixed posterior is also proposed, and the method is shown to produce accurate, calibrated posteriors on toy and real scientific simulators.

By Jan Boelts, Cornelius Schr\"oder, Jonas Beck, Jakob H. Macke, Michael Deistler, Daniel Gedon