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Making inferences from data streams is a pervasive problem in many moderndata analysis applications. But it requires to address the problem ofcontinuous model updating and adapt to changes or drifts in the underlying datagenerating distribution. In this paper, we approach these problems from aBayesian perspective covering general conjugate exponential models. Ourproposal makes use of non-conjugate hierarchical priors to explicitly modeltemporal changes of the model parameters. We also derive a novel variationalinference scheme which overcomes the use of non-conjugate priors whilemaintaining the computational efficiency of variational methods over conjugatemodels. The approach is validated on three real data sets over three latentvariable models.

Bayesian Models of Data Streams with Hierarchical Power Priors