Rank conjecture for the Fisher information matrix of the Poisson canonical polyadic model
Consider the Fisher information matrix from the stated theorem, of size . Let have strictly positive components, and suppose that no two distinct subsets of its components are linearly dependent.
Fisher information rank conjecture. The rank is
where if , and if .
The rank determines the dimensionality of the parameter space relevant to inference and therefore gives insight into the model's complexity and identifiability. The claim is proved in the special case , while numerical evidence is provided for ranks greater than one; the general claim remains open in the supplied text.
References
Primary source
Carlos Llosa-Vite, Daniel M. Dunlavy, Richard B. Lehoucq, Oscar López and Arvind Prasadan, “A Latent-Variable Formulation of the Poisson Canonical Polyadic Tensor Model: Maximum Likelihood Estimation and Fisher Information”, arXiv:2511.05352 (2025).
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