Automatic Backward Filtering Forward Guiding for Markov processes and graphical models

Understand

We incorporate discrete and continuous time Markov processes as building blocks into probabilistic graphical models with latent and observed variables.

• We introduce the automatic Backward Filtering Forward Guiding (BFFG) paradigm (Mider et al., 2021) for programmable inference on latent states and model parameters.
• Our starting point is a generative model, a forward description of the probabilistic process dynamics.
• We backpropagate the information provided by observations through the model to transform the generative (forward) model into a pre-conditional model guided by the data.