Reviews: Attentive State-Space Modeling of Disease Progression

The key idea in this paper is to maintain this property of discrete state space models while relaxing the stationary Markov assumption on the transition probabilities that we typically use to simplify inference. Although this idea is not new (e.g. The variational inference algorithm for this model also seems to be new. In practice, we can relax the "strict" Markov assumption (i.e. the state in year t 1 is conditionally independent of the past given the state at year t) by augmenting the state with the past h 1 years. This keeps the inference exact and relatively easy to implement.