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We define the Dynamical Chinese Restaurant Process (Dynamical CRP), a novel stochastic process that provides a non-stationary prior over cluster assignments and yields an efficient streaming variational inference algorithm. Experiments show the Dynamical CRP can be applied on diverse synthetic and real data with Gaussian and non-Gaussian likelihoods.

R-IBP is a novel recursive form of the Indian Buffet Process that makes feature models applicable to streaming data. It enables creation of new features online and in a probabilistic, principled manner. As a prior for feature models, R-IBP yields efficient inference over an unbounded number of latent features, with quasilinear average time complexity and logarithmic average space complexity.

The first experimentally quantitative index of metacognition of emotion. Advancing understanding of awareness toward subjective feelings.