  There are multiple real-world problems in which training data is unavailable,
and still, the ambition is to learn values of the system parameters, at which
test data on an observable is realised, subsequent to the learning of the
functional relationship between these variables. We present a novel Bayesian
method to deal with such a problem, in which we learn a system function of a
stationary dynamical system, for which only test data on a vector-valued
observable is available, and training data is unavailable. This exercise
borrows heavily from the state space probability density function ($pdf$), that
we also learn. As there is no training data available for either sought
function, we cannot learn its correlation structure, and instead, perform
inference (using Metropolis-within-Gibbs), on the discretised form of the
sought system function and of the ${pdf}$, where this $pdf$ is constructed such
that the unknown system parameters are embedded within its support. Likelihood
of the unknowns given the available data, is defined in terms of such a
${pdf}$. We make an application to the learning of the density of all
gravitational matter in a real galaxy.
