In order to be predictive, epidemic models rely on valid values for parameters governing outbreak dynamics, conditional on the population structure. However, obtaining these parameters is complicated by the fact that even when knowing the underlying contact network structure, infection events are censored-it is only when disease is detected either from symptoms or laboratory tests that a case becomes apparent. In attempting to surmount this difficulty, parameter estimates are often obtained by making strong assumptions as to the infectious period or through ad hoc methods with unknown certainty. Measuring the uncertainty in such estimates is as important as obtaining the estimates themselves in providing an honest risk prediction. Given these difficulties, inference for epidemic processes has perhaps received little attention in comparison to its simulation counterpart.