This paper presents a new approach to robust Gaussian process regression, creating non-parametric Bayesian regression estimate outliers. Most existing approaches replace an outlier-prone likelihood with non-Gaussian induced from heavy tail distribution, such as the Laplace distribution and Student-t distribution. However, use of would incur need for computationally expensive approximate computa...