  This paper considers the problem of inferring an unknown network of dynamical
systems driven by unknown, intrinsic, noise inputs. Equivalently we seek to
identify direct causal dependencies among manifest variables only from
observations of these variables. For linear, time-invariant systems of minimal
order, we characterise under what conditions this problem is well posed. We
first show that if the transfer matrix from the inputs to manifest states is
minimum phase, this problem has a unique solution irrespective of the network
topology. This is equivalent to there being only one valid spectral factor (up
to a choice of signs of the inputs) of the output spectral density.
  If the assumption of phase-minimality is relaxed, we show that the problem is
characterised by a single Algebraic Riccati Equation (ARE), of dimension
determined by the number of latent states. The number of solutions to this ARE
is an upper bound on the number of solutions for the network. We give necessary
and sufficient conditions for any two dynamical networks to have equal output
spectral density, which can be used to construct all equivalent networks.
Extensive simulations quantify the number of solutions for a range of problem
sizes. For a slightly simpler case, we also provide an algorithm to construct
all equivalent networks from the output spectral density.
