  Effective field theories (EFTs) organize the description of complex systems
into an infinite sequence of decreasing importance. Predictions are made with a
finite number of terms, which induces a truncation error that is often left
unquantified. We formalize the notion of EFT convergence and propose a Bayesian
truncation error model for predictions that are correlated across the
independent variables, e.g., energy or scattering angle. Central to our
approach are Gaussian processes that encode both the naturalness and
correlation structure of EFT coefficients. Our use of Gaussian processes
permits efficient and accurate assessment of credible intervals, allows EFT
fits to easily include correlated theory errors, and provides analytic
posteriors for physical EFT-related quantities such as the expansion parameter.
We demonstrate that model-checking diagnostics---applied to the case of
multiple curves---are powerful tools for EFT validation. As an example, we
assess a set of nucleon-nucleon scattering observables in chiral EFT. In an
effort to be self contained, appendices include thorough derivations of our
statistical results. Our methods are packaged in Python code, called gsum, that
is available for download on GitHub.
