The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe
Many domains of science have developed complex simulations to describe phenomena of interest.
While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems.
We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field.
Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.
statistical inference | implicit models | likelihood-free inference |
approximate Bayesian computation | neural density estimation
Mechanistic models can be used to predict how systems will behave in a variety of circumstances.
These run the gamut of distance scales, with notable examples including particle physics, molecular dynamics, protain folding, population genetics, neuroscience, epidemiology, economics, ecology, climate science, astrophysics, and cosmology.
The expressiveness of programming languages facilitates the development of complex, high-fidelity simulations and the power of modern computing provides the ability to generate synthetic data from them.
Unfortunately, these simulators are poorly suited for statistical inference.
The source of the challenge is that the probability density (or likelihood) for a given observation - an essential ingredient for both frequentist and Bayesian inference methods - is typically intractable.
Such models are often referred to as implicit models and contrasted against prescribed models where the likelihood for an observation can be explicitly calculated (1).
The problem setting of statistical inference under intractable likelihoodshas been dubbed likelihood-free inference - although it is a bit of a misnomer as typically one attempts to estimate the intractable likelihood, so we feel the term simulation-based inference is more apt.
＊ABCは、Approximate Bayesian Computationのこと。
Workflows for Simulation-Based Inference