latentflow-6d90034a·1 events·first seen Aliases: LatentFlow
LatentFlow is a new general framework for conditioning stochastic processes without learned neural approximations or training. The approach maps a stochastic process to a tractable latent innovation space, reduces conditioning to latent-space inference via a guided probability flow, and pushes samples forward — yielding provably exact conditioning at the level of the target law. The method is claimed to work across a broad range of model classes (spatial priors, stochastic PDEs, mechanistic models, point processes) and runs in seconds on a single CPU.