Maximum likelihood method for parameter estimation of bell-shaped functions on graphs

To estimate the location-scale parameters of a bell-shaped density on attributed graphs, we consider radial densities as approximations. The problem of estimating the parameters of radial densities on graphs is equivalent to the problem of estimating the parameters of truncated Gaussians in a Euclidean space. Based on this result, we adopt the maximum likelihood method for truncated Gaussians. From the estimated probabilities we inferred the conditional probabilities for a Bayes classifier. Experiments on random graphs and four benchmark data sets of the IAM graph database repository and on random weighted graphs are presented and discussed.

► Maximum likelihood framework for estimating bell-shaped densities on attributed graphs.
► Estimating parameters of radial densities on graphs is equivalent to estimating parameters of truncated Gaussians on vectors.
► Probability estimates result in simple and fast Bayes classifier with satisfactory classification accuracy.