Therefore, assessments about the relative likelihood of a given transformation compared to a set of other transformations can be made while dealing with the noise in the data in a statistically rigorous manner. By eliminating as much peripheral data from the evaluation process as possible the application of SBMs to the network allows the elimination of as many error sources as possible. Though the results are influenced by the researcher's choices of transformations to be tested, the method is able to efficiently separate likely from unlikely transformations. This allows both a thorough scan of broad areas of chemical space to find a high-likelihood set for in-depth investigation, as well as, the evaluation of a more well-defined set to evaluate chemical intuition against statistical insights. This work presents the first application of these methods to reaction prediction and, we believe, makes an important contribution to the field of reaction prediction.