A number of precursor-type methods for solar-cycle prediction are based on the use of regression models and confidence-level estimates. A drawback of these methods is that they do not permit one to make probability statements, nor do they offer straightforward ways to propagate the uncertainty from observations to the quantities of interest. We suggest a method for calculating the probability of the maximum amplitude of Solar Cycle 25 using Bayesian inference. We illustrate this approach with the predictions made by one particular phenomenological model that relates the time interval between the termination events of preceding cycles to the amplitude of the next cycle. Our results show well-constrained posterior-predictive distributions for the maximum sunspot number. The impact of uncertainty on the sunspot number and the time interval between terminators is quantified. A comparison between the maximum sunspot numbers of the past and the posterior-predictive distributions computed using the method enables us to quantify the quality of the inference and the prediction.