  We establish connections between invariant theory and maximum likelihood
estimation for discrete statistical models. We show that norm minimization over
a torus orbit is equivalent to maximum likelihood estimation in log-linear
models. We use notions of stability under a torus action to characterize the
existence of the maximum likelihood estimate, and discuss connections to
scaling algorithms.
