  The goal of AIMS (Asteroseismic Inference on a Massive Scale) is to estimate
stellar parameters and credible intervals/error bars in a Bayesian manner from
a set of asteroseismic frequency data and so-called classical constraints. To
achieve reliable parameter estimates and computational efficiency, it searches
through a grid of pre-computed models using an MCMC algorithm -- interpolation
within the grid of models is performed by first tessellating the grid using a
Delaunay triangulation and then doing a linear barycentric interpolation on
matching simplexes. Inputs for the modelling consist of individual frequencies
from peak-bagging, which can be complemented with classical spectroscopic
constraints. AIMS is mostly written in Python with a modular structure to
facilitate contributions from the community. Only a few computationally
intensive parts have been rewritten in Fortran in order to speed up
calculations.
