Posteriori Partition

A posteriori partitioning aims to divide a data space into regions exhibiting distinct outcomes, focusing on identifying robust and meaningful partitions rather than a single "optimal" one. Current research emphasizes Bayesian approaches, incorporating prior knowledge and exploring the space of near-optimal partitions using methods like Rashomon sets and variational Bayes, particularly within the context of deep learning models and complex systems like turbulent flows. This work is significant for improving the reliability and interpretability of analyses across diverse fields, from causal inference and climate modeling to rare event estimation and efficient deep learning model deployment.