Self-interacting processes via Doob conditioning

We connect self-interacting processes, that is, stochastic processes where transitions depend on the time spent by a trajectory in each configuration, to Doob conditioning. In this way we demonstrate that Markov processes with constrained occupation measures are realised optimally by self-interacting dynamics. We use a tensor network framework to guide our derivations. We illustrate our general results with new perspectives on well-known examples of self-interacting processes, such as random walk bridges, excursions, and forced excursions.