Tensorized forward filtering for factorial HMMs reduces per-iteration cost from O(M^2) to O(M * sum(M_k)) by exploiting the Kronecker structure of the transition matrix directly via tensor contractions.
SIAM Journal on Matrix Analysis and Applications , volume =
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Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models
Tensorized forward filtering for factorial HMMs reduces per-iteration cost from O(M^2) to O(M * sum(M_k)) by exploiting the Kronecker structure of the transition matrix directly via tensor contractions.