swyft: Truncated Marginal Neural Ratio Estimation inPython
B. Miller, Alex Cole, Christoph Weniger, Francesco Nattino, Ou Ku, Meiert W. Grootes
Abstract
Parametric stochastic numerical simulators are ubiquitous in science. They model observed phenomena by mapping a parametric representation of simulation conditions to a hypothetical observation-effectively sampling from a probability distribution over observational data known as the likelihood. Simulators are advantageous because they easily encode relevant scientific knowledge. Simulation-based inference (SBI) is a machine learning technique which applies a simulator, a fitted statistical surrogate model, and a set of prior beliefs to estimate a probabilistic description of the parameters which plausibly generated some observational data. This description of parameters is known as the posterior and it is the end-product of Bayesian inference.