Poisson-Gamma Dynamical Systems with Non-Stationary Transition Dynamics

arXiv:2402.16297v1 Announce Type: new
Abstract: Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data. Among these Bayesian models, Poisson-Gamma Dynamical Systems (PGDSs) are proven to be effective in capturing the evolving dynamics underlying observed count sequences. However, the state-of-the-art PGDS still falls short in capturing the time-varying transition dynamics that are commonly observed …

abstract arxiv bayesian count cs.ai cs.lg data dynamics series systems time series transition type