Bayesian inference in vision

Bayesian inference in vision is a way our brains interpret what we see by combining prior knowledge with new visual information. It works like updating a belief: we start with expectations based on experience, then adjust them as we gather more data from our eyes. For example, if we see a blurry shape in low light, our brain uses what it already knows about similar objects to guess what it might be. This process helps us perceive the world accurately even when visual data is incomplete or uncertain.