  This paper presents a Cramer-Rao lower bound (CRLB) on the variance of
unbiased estimates of factor matrices in Canonical Polyadic (CP) or
CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy observations,
(i.e., the tensor plus a random Gaussian i.i.d. tensor). A novel expression is
derived for a bound on the mean square angular error of factors along a
selected dimension of a tensor of an arbitrary dimension. The expression needs
less operations for computing the bound, O(NR^6), than the best existing
state-of-the art algorithm, O(N^3R^6) operations, where N and R are the tensor
order and the tensor rank. Insightful expressions are derived for tensors of
rank 1 and rank 2 of arbitrary dimension and for tensors of arbitrary dimension
and rank, where two factor matrices have orthogonal columns.
  The results can be used as a gauge of performance of different approximate CP
decomposition algorithms, prediction of their accuracy, and for checking
stability of a given decomposition of a tensor (condition whether the CRLB is
finite or not). A novel expression is derived for a Hessian matrix needed in
popular damped Gauss-Newton method for solving the CP decomposition of tensors
with missing elements. Beside computing the CRLB for these tensors the
expression may serve for design of damped Gauss-Newton algorithm for the
decomposition.
