HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.
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Hierarchical excitatory processes for modelling event-time data in the presence of exogenous stimuli
HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.