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Bayesian networks are a popular model for reasoning under uncertainty. We study the problem of efficient probabilistic inference with these models when some of the conditional probability tables represent deterministic or noisy -out-of- functions. These tables appear naturally in real-world applications when we observe a state of a variable that depends on its parents via an addition or noisy addition relation. We provide a lower bound of the rank and an upper bound for the symmetric border rank...
Let be the complex vector space of homogeneous linear polynomials in the variables . Suppose is a subgroup of , and is an irreducible character of . Let be the symmetry class of polynomials of degree with respect to and . For any linear operator acting on , there is a (unique) induced operator acting on symmetrized decomposable polynomials by
In this paper, we show that the representation of the general linear group is equivalent to the direct sum of copies of a representation...
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