{\sc ampere}: A tool to fit heterogeneous observations consistently

As astronomy advances and data becomes more complex, models and inference also become more expensive and complex. In this paper we present {\sc ampere}, which aims to solve this problem using modern inference techniques such as flexible likelihood functions and likelihood-free inference. {\sc ampere}\ can be used to do Bayesian inference even with very expensive models (hours of CPU time per model) that do not include all the features of the observations (e.g. missing lines, incomplete descriptions of PSFs, etc). We demonstrate the power of \ampere\ using a number of simple models, including inferring the posterior mineralogy of circumstellar dust using a Monte Carlo Radiative Transfer model. {\sc ampere}\ reproduces the input parameters well in all cases, and shows that some past studies have tended to underestimate the uncertainties that should be attached to the parameters. {\sc ampere}\ can be applied to a wide range of problems, and is particularly well-suited to using expensive models to interpret data.