A Gaussian process is a stochastic process whose realizations consist of random values associated with
every point in a range of times (or of space) such that each such random variable has a normal
distribution. Moreover, every finite collection of those random variables has a multivariate normal
distribution. A random field is a generalization of a stochastic process such that the underlying parameter
need no longer be a simple real or integer valued "time", but can instead take values that are
multidimensional vectors, or points on some manifold. This GaussianField class is a generalization, as well
as the underlying implementation, of
GaussianProcess. Prior to calling
getDistribution(Comparable...) you must call
addObservation(Comparable, double)
one or more times.