  We introduce a technique for gravitational-wave analysis, where Gaussian
process regression is used to emulate the strain spectrum of a stochastic
background using population-synthesis simulations. This leads to direct
Bayesian inference on astrophysical parameters. For PTAs specifically, we
interpolate over the parameter space of supermassive black-hole binary
environments, including 3-body stellar scattering, and evolving orbital
eccentricity. We illustrate our approach on mock data, and assess the prospects
for inference with data similar to the NANOGrav 9-yr data release.
