  The $p$-tensor Ising model is a one-parameter discrete exponential family for
modeling dependent binary data, where the sufficient statistic is a
multi-linear form of degree $p \geq 2$. This is a natural generalization of the
matrix Ising model, that provides a convenient mathematical framework for
capturing higher-order dependencies in complex relational data. In this paper,
we consider the problem of estimating the natural parameter of the $p$-tensor
Ising model given a single sample from the distribution on $N$ nodes. Our
estimate is based on the maximum pseudo-likelihood (MPL) method, which provides
a computationally efficient algorithm for estimating the parameter that avoids
computing the intractable partition function. We derive general conditions
under which the MPL estimate is $\sqrt N$-consistent, that is, it converges to
the true parameter at rate $1/\sqrt N$. In particular, we show the $\sqrt
N$-consistency of the MPL estimate in the $p$-spin Sherrington-Kirkpatrick (SK)
model, spin systems on general $p$-uniform hypergraphs, and Ising models on the
hypergraph stochastic block model (HSBM). In fact, for the HSBM we pin down the
exact location of the phase transition threshold, which is determined by the
positivity of a certain mean-field variational problem, such that above this
threshold the MPL estimate is $\sqrt N$-consistent, while below the threshold
no estimator is consistent. Finally, we derive the precise fluctuations of the
MPL estimate in the special case of the $p$-tensor Curie-Weiss model. An
interesting consequence of our results is that the MPL estimate in the
Curie-Weiss model saturates the Cramer-Rao lower bound at all points above the
estimation threshold, that is, the MPL estimate incurs no loss in asymptotic
efficiency, even though it is obtained by minimizing only an approximation of
the true likelihood function for computational tractability.
