Data augmentation-based statistical inference of diffusion processes.

The identification of diffusion processes is challenging for many real-world systems with sparsely sampled observation data. In this work, we propose a data augmentation-based sparse Bayesian learning method to identify a class of diffusion processes from sparsely sampled data. We impute latent unsampled diffusion paths between adjacent observations and construct a candidate model for the diffusion processes with the sparsity-inducing prior on model parameters.