Discrete Model

Discrete models offer a powerful approach to representing and analyzing systems with discrete states or events, even when underlying behavior is continuous. Current research focuses on developing efficient algorithms for inference in these models, including those based on probability generating functions and iterative methods that decouple discrete and continuous components. Applications range from analyzing the mechanics of soft robots and improving image processing algorithms to modeling collective adaptive systems and enabling robust robot perception. These advancements are improving the accuracy and efficiency of simulations and inference in diverse fields, leading to better designs and more effective solutions.