We conduct a probabilistic analysis of an extension of the classical logistic model by considering all its parameters as continuous random variables with a joint probability density function estimated by Bayesian sampling techniques. Given the stochastic nature of the solution, we will derive its first probability density function using the random variable transformation technique, enabling us to achieve a detailed probabilistic characterization of the model. We then apply our theoretical findings to model the growth of social networks using real-world data.