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Abstract
Bayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.
Highlights
Change-point detection (CPD) methods aim to identify abrupt transitions in sequences of observations, for both univariate and multivariate cases
The methods considered for the comparison are i) the Bayesian CPD algorithm [1], ii) Hierarchical CPD [11], iii) the infinite-dimensional method of [10] and iv) the Multinomial-based approach proposed in this work
Under the assumption that CPs lie in a lower-dimensional manifold, inference is carried out with pseudo-observations based on posterior point-estimates of the latent variables given the data
Summary
Change-point detection (CPD) methods aim to identify abrupt transitions in sequences of observations, for both univariate and multivariate cases. The identifiability of change-points (CP) is directly related to the discrepancy between the distributions governing each partition. In this context, the Bayesian framework provides a reliable solution to obtain uncertainty measures over both the parameters and the CP locations. The Bayesian online CPD algorithm (BOCPD) introduced in [1] uses this idea to derive a recursive exact inference method. When observations become high-dimensional and the number of parameters in the model grows exponentially, there is not enough evidence in the sequential data to obtain reliable estimates of the true generative parameters
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