Innovation processes for inference

06/08/2023

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by   Giulio Tani Raffaelli, et al.

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In this letter, we introduce a new approach to quantify the closeness of symbolic sequences and test it in the framework of the authorship attribution problem. The method, based on a recently discovered urn representation of the Pitman-Yor process, is highly accurate compared to other state-of-the-art methods, featuring a substantial gain in computational efficiency and theoretical transparency. Our work establishes a clear connection between urn models critical in interpreting innovation processes and nonparametric Bayesian inference. It opens the way to design more efficient inference methods in the presence of complex correlation patterns and non-stationary dynamics.