Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models

We study the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models, with the goal of generating new sample paths from training data consisting of N independent trajectories observed on a high-frequency discrete time grid.