  Effective forces -- derived from experimental or {\it in silico} molecular
dynamics time traces -- are critical in developing reduced and computationally
efficient descriptions of otherwise complex dynamical problems. Thus, designing
methods to learn effective forces efficiently from time series data is
important. Of equal importance is the fact that methods should be suitable in
inferring forces for undersampled regions of the phase space where data are
limited. Ideally, a method should {\it a priori} be minimally committal as to
the shape of the effective force profile, exploit every data point without
reducing data quality through any form of binning or pre-processing, and
provide full credible intervals (error bars) about the prediction. So far no
method satisfies all three criteria. Here we propose a generalization of the
Gaussian process (GP), a key tool in Bayesian nonparametric inference and
machine learning, to achieve this for the first time.
