Generic Stochastic

Generic stochastic methods address the challenge of modeling and solving problems involving inherent randomness and uncertainty, particularly in complex systems. Current research focuses on developing and improving algorithms like Bayesian computation, stochastic dynamic programming, and variants of Monte Carlo methods, often tailored to specific applications such as traffic flow modeling or federated learning in resource-constrained environments. These advancements aim to enhance the efficiency and accuracy of solving optimization problems across diverse fields, from transportation to machine learning, by effectively handling stochasticity and improving the exploration of complex solution spaces. The resulting improvements in model accuracy and computational efficiency have significant implications for various scientific and engineering disciplines.