A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.