Physics ∩ ML

a virtual hub at the interface of theoretical physics and deep learning.

16 Feb 2022

Fast and Credible Inference with Truncated Marginal Neural Ratio Estimation

Alex Cole, University of Amsterdam

Across fields, scientific models are computationally implemented via parametric stochastic simulators. However, solving the “inverse problem” and constraining model parameters from data is a challenge in this context. Recently, the field of simulation-based inference has made great strides thanks to deep learning methods. I will outline a new method in simulation-based inference called Truncated Marginal Neural Ratio Estimation (TMNRE). TMNRE is (i) simulation-efficient, actively identifying the relevant regime of parameter space without sacrificing amortization (ii) scalable to high-dimensional data and model parameter spaces (iii) trustworthy, in the sense that statistical consistency tests beyond those available to e.g. MCMC can be rapidly performed. I will show examples of these benefits in the context of cosmological inference. I will also describe our development of a user-friendly and general package for TMNRE called swyft.

Implementation of TMNRE available at Talk based on (NeurIPS ML4PS ’20), (NeurIPS ’21),