Inference Method

Inference methods aim to estimate underlying parameters or distributions from observed data, a crucial task across diverse scientific fields. Current research focuses on improving efficiency and scalability, particularly for complex models and large datasets, employing techniques like deep generative models, federated learning with prototype aggregation, and sequential neural posterior estimation with truncated proposals. These advancements are impacting various applications, from robotics and game theory to scientific modeling, by enabling more accurate and efficient estimation of parameters governing complex systems.