  This paper proposes a new algorithm for Gaussian process classification based
on posterior linearisation (PL). In PL, a Gaussian approximation to the
posterior density is obtained iteratively using the best possible linearisation
of the conditional mean of the labels and accounting for the linearisation
error. PL has some theoretical advantages over expectation propagation (EP):
all calculated covariance matrices are positive definite and there is a local
convergence theorem. In experimental data, PL has better performance than EP
with the noisy threshold likelihood and the parallel implementation of the
algorithms.
