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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. On the theor
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