The Eisner algorithm, originally designed for generative parsing, decomposes the probability of a dependency parse into the probabilities of each attachment of a dependent to its parent, and the probabilities of each parent stopping taking dependents.
the model parameters, θ , are normally distributed, with mean µ (typically zero) and variance σ 2 .