A latent force model is a Gaussian process with a covariance function
inspired by a differential operator. Such covariance function is obtained by
performing convolution integrals between Green's functions associated to the
differential operators, and covariance functions associated to latent
functions. In the classical formulation of latent force models, the covariance
functions are obtained analytically by solving a double integral, leading to
expressions that involve numerical solutions of different types of error
functions. In consequence, the covariance matrix calculation is considerably
expensive, because it requires the evaluation of one or more of these error
functions. In this paper, we use random Fourier features to approximate the
solution of these double integrals obtaining simpler analytical expressions for
such covariance functions. We show experimental results using ordinary
differential operators and provide an extension to build general kernel
functions for convolved multiple output Gaussian processes.Comment: 10 pages, 4 figures, accepted by UAI 201