  We present a distributed (non-Bayesian) learning algorithm for the problem of
parameter estimation with Gaussian noise. The algorithm is expressed as
explicit updates on the parameters of the Gaussian beliefs (i.e. means and
precision). We show a convergence rate of $O(1/k)$ with the constant term
depending on the number of agents and the topology of the network. Moreover, we
show almost sure convergence to the optimal solution of the estimation problem
for the general case of time-varying directed graphs.
