  Part I of this paper discusses the problem of learning the operational
structure of the grid from nodal voltage measurements. In this work (Part II),
the learning of the operational radial structure is coupled with the problem of
estimating nodal consumption statistics and inferring the line parameters in
the grid. Based on a Linear-Coupled (LC) approximation of AC power flows
equations, polynomial time algorithms are designed to complete these tasks
using the available nodal complex voltage measurements. Then the structure
learning algorithm is extended to cases with missing data, where available
observations are limited to a fraction of the grid nodes. The efficacy of the
presented algorithms are demonstrated through simulations on several
distribution test cases.
