Jeffrey’s Rule

Jeffrey’s Rule is a way to update beliefs when new evidence doesn’t fully confirm or deny a hypothesis but adjusts its probability. Imagine originally assigning a belief (probability) to an event, and then receiving new information that affects related probabilities without completely certifying or eliminating the event. Jeffrey’s Rule allows you to revise your initial probabilities proportionally, based on the new evidence, maintaining consistency across all related beliefs. It’s useful in scenarios where information is uncertain or probabilistic, enabling more nuanced belief updates than simply confirming or rejecting a hypothesis outright.