Jeffrey conditionalization

Jeffrey conditionalization is a method for updating beliefs when new evidence is available, particularly when that evidence is uncertain or probabilistic. Instead of simply revising a belief based on a definite event, it adjusts beliefs based on the likelihood of various outcomes. For example, if you have a belief about rain tomorrow, and you receive information that suggests there's a 70% chance of rain, Jeffrey conditionalization allows you to update your belief about rain based on this probability, leading to a more nuanced understanding of what you believe given the new information.