Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design
Variational Inference for Interacting Particle Systems with Discrete Latent States
Giosue Migliorini · Padhraic Smyth
Keywords: [ Variational Inference ] [ continuous-time markov chains ] [ spatiotemporal models ] [ schroedinger bridge ]
We present a novel Bayesian learning framework for interacting particle systems with discrete latent states, addressing the challenge of inferring dynamics from partial, noisy observations. Our approach learns a variational posterior path measure by parameterizing the generator of the underlying continuous-time Markov chain. We formulate the problem as a multi-marginal Schrödinger bridge with aligned samples, employing a two-stage learning procedure. Our method incorporates an emission distribution for decoding latent states and uses a scalable variational approximation.