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• W2950586481 abstract "We propose moment-based variational inference as a flexible framework for approximate smoothing of latent Markov jump processes. The main ingredient of our approach is to partition the set of all transitions of the latent process into classes. This allows to express the Kullback-Leibler divergence between the approximate and the exact posterior process in terms of a set of moment functions that arise naturally from the chosen partition. To illustrate possible choices of the partition, we consider special classes of jump processes that frequently occur in applications. We then extend the results to parameter inference and demonstrate the method on several examples." @default.
• W2950586481 created "2019-06-27" @default.
• W2950586481 creator A5070544702 @default.
• W2950586481 creator A5081023733 @default.
• W2950586481 date "2019-05-14" @default.
• W2950586481 modified "2023-10-01" @default.
• W2950586481 title "Moment-Based Variational Inference for Markov Jump Processes" @default.
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