Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors

arXiv:2410.06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processing and machine learning. Modelling the joint PMF as a tensor that admits a low-rank canonical polyadic decomposition (CPD) has enabled the development of efficient PMF estimation algorithms. However, these algorithms require the rank (model order) of the tensor to be specified beforehand. In real-world applications, the true rank is unknown. Therefore, an appr

Source: arXiv cs.LG — read the full report at the original publisher.

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