def k_xf(self,t,tprime,s,l):
return <NUM_LIT:0.5> * np.sqrt(math.pi) * l * (math.erf((t-tprime)/l) + math.erf((tprime-s)/l))<EOL>
Covariance between the gradient (latent value) and the actual (observed) value.

        Note that sprime isn't actually used in this expression, presumably because the 'primes' are the gradient (latent) values which don't
        involve an integration, and thus there is no domain over which they're integrated, just a single value that we want.