Incremental inference of collective graphical models
This addresses incremental inference for collective dynamics from aggregate data, which is an incremental improvement over existing methods.
The paper tackles the problem of estimating aggregate marginals of a Markov chain from noisy aggregate observations in an online fashion, proposing a sliding window Sinkhorn belief propagation algorithm that demonstrates performance on applications like inferring population flow.
We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chain from noisy aggregate observations in an incremental (online) fashion. We propose a sliding window Sinkhorn belief propagation (SW-SBP) algorithm that utilizes a sliding window filter of the most recent noisy aggregate observations along with encoded information from discarded observations. Our algorithm is built upon the recently proposed multi-marginal optimal transport based SBP algorithm that leverages standard belief propagation and Sinkhorn algorithm to solve inference problems from aggregate data. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations.