This paper explores the effects of parameter sharing on Bayesian network (BN) parameter learning when there is incomplete data. Using the Expectation Maximization (EM) algorithm, we investigate how varying degrees of parameter sharing, varying number of hidden nodes, and different dataset sizes impact EM performance. The specific metrics of EM performance examined are: likelihood, error, and the number of iterations required

for convergence. These metrics are important in a number of applications, and we emphasize learning of BNs for diagnosis of electrical power systems. One main point, which we investigate both analytically and empirically,

is how parameter sharing impacts the error associated with EM’s parameter estimates.