This paper presents a novel framework for modelling and stochastic updating of nonlinear systems in structural dynamics, with particular emphasis on joint structures. The key innovation lies in the integration of experimental data, system identification, and probabilistic methods to develop a comprehensive understanding of nonlinear dynamic behaviour. The methodology employs a specialised control system to extract backbone curves, which characterise the system’s fundamental nonlinear response. A data-driven approach is implemented to identify the dominant system dynamics, which is then seamlessly integrated with the control system to create an analytical model. A significant contribution of this work is the development of a stochastic framework that combines the analytical model with measured system responses through probabilistic sampling methods, enabling robust uncertainty quantification. To address the computational challenges inherent in such complex simulations, the framework incorporates a deep learning model trained on both experimental and analytical data. This integration substantially improves computational efficiency while maintaining accuracy in predicting nonlinear dynamic responses. The framework’s effectiveness is demonstrated through application to jointed structures, where traditional deterministic approaches often fall short. By providing a probabilistic perspective on system behaviour, this methodology offers more reliable predictions of dynamic responses under varying conditions. The successful implementation of this approach represents a significant advancement in the field of structural dynamics, particularly for complex systems where uncertainty quantification is crucial for accurate response prediction.