A number of precursor-type methods for solar-cycle prediction are based on the use of regression models and confidence-level estimates. A draw-back to these methods is that they do not permit one to make probability statements, nor do they offer straightforward ways to propagate the uncertainty from the observations to the quantities of interest. We suggest a method to calculate the probability for 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 termination events of preceding cycles with the amplitude of the next cycle. Our results show well-constrained posterior-predictive distributions for the maximum sunspot-number. The impact of uncertainty on sunspot-number and time interval between terminators is quantified. A comparison between past maximum sunspot-number values and posterior-predictive distributions computed using the method enables us to quantify the quality of the inference and of the prediction.