“…The former class includes classical ODE solvers that are stochastically perturbed (Abdulle and Garegnani, 2020, Conrad et al, 2017, Lie et al, 2019, Teymur et al, 2018, solvers that approximately sample from a Bayesian inference problem (Tronarp et al, 2019b), and solvers that perform Gaussian process regression on stochastically generated data (Chkrebtii et al, 2016). On the other hand, deterministic solvers formulate the problem as a Gaussian process regression problem, either with a data generation mechanism (Hennig and Hauberg, 2014, Kersting and Hennig, 2016, Magnani et al, 2017, Schober et al, 2014, Skilling, 1992 or by attempting to constrain the estimate to solve the ODE at each point on the mesh (John et al, 2019, Tronarp et al, 2019b. For computational reasons it is fruitful to select the Gaussian process prior to be of Markov type (Kersting and Hennig, 2016, Magnani et al, 2017, Tronarp et al, 2019b, as this reduces cost of inference from O(N 3 ) to O(N ) Särkkä, 2010, Särkkä et al, 2013).…”