Uncertainty propagation in auto-regressive random neural network models
Analytical and particle-based methods for uncertainty propagation in random neural network models, where both the inputs and network parameters are allowed to be random, are developed and extended to autonomous dynamical systems whose one-step evolution map is represented by a random neural network.