Abstract
Graphs are important mathematical tools for modelling processes. An important issue in this area is to infer the changes that occur in the underlying generative process. In this work, inference of multiple change points in stochastic block graph time series is studied. A well-known algorithm for inference in time series is the forward-backward algorithm. In order to decrease computational complexity of this algorithm in graphical models, backward smoothing part is replaced with backward Monte Carlo sampling. With the experiments, it is observed that modified algorithm gives result in accordance with the real data.
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