A probabilistic framework is developed for the prognosis of battery packs. It is demonstrated using aging campaign data, that aging models alone may not be sufficient for aging prognosis, and aging model parameter estimation may further improve the accuracy of prognosis. A systematic framework that extends the aging models to battery pack aging and prognosis still remains challenging. We propose a framework that bridges the gap in cell and pack aging prognosis in a probabilistic sense, and further improves the prognosis by estimating the aging model parameters for the pack. The framework is versatile for various applications because it is not restricted to a specific cell chemistry, or a type of aging model. In addition, the proposed framework could distinguish more aged cells as compared to other cells in the pack. Numerical examples are provided to demonstrate the effectiveness of the proposed framework.