Gaussian process classification using posterior linearisation

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. Considering three widely-used likelihood functions, in general, PL provides lower classification errors in real data sets than expectation propagation and Laplace algorithms.