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Maximum Likelihood Estimation Of A Low-Rank Probability Mass Tensor From Partial Observations

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IEEE ICASSP 2020 Virtual Conference May 2020

We consider the estimation of the Probability Mass Function (PMF) of a discrete random vector from partial observations. Since the PMF takes the form of a multi-way tensor, under certain model assumptions the problem becomes closely related to tensor fact

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Maximum Likelihood Estimation Of A Low-Rank Probability Mass Tensor From Partial Observations

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We consider the estimation of the Probability Mass Function (PMF) of a discrete random vector from partial observations. Since the PMF takes the form of a multi-way tensor, under certain model assumptions the problem becomes closely related to tensor fact