This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed (GP) priors on length scales and magnitudes of next level processes in hierarchy. The idea represent as non-linear hierarchical system linear stochastic differential equations (SDEs), where each SDE corresponds conditional GP. regression probl...