Scalable inference with Autoregressive Neural Ratio Estimation

Understand

In recent years, there has been a remarkable development of simulation-based inference (SBI) algorithms, and they have now been applied across a wide range of astrophysical and cosmological analyses.

• There are a number of key advantages to these methods, centred around the ability to perform scalable statistical inference without an explicit likelihood.
• In this work, we propose two technical building blocks to a specific sequential SBI algorithm, truncated marginal neural ratio estimation (TMNRE).
• In particular, first we develop autoregressive ratio estimation with the aim to robustly estimate correlated high-dimensional posteriors.