Generate a regression dataset by sampling a function from a GP prior and then adding some noise. Use ABC to sample from the posterior. Each sample should be generated by first sampling from the GP prior and then adding noise. Use the Euclidean distance between the generated data and the original data to determine acceptance (experiment with different levels of tolerance). How efficient is your sampler? How do the samples compare with those from the true posterior?