Bayesian Filters

Bayesian filters are algorithms that estimate the state of a system over time by combining prior knowledge with new data. They update their beliefs as more information becomes available, using probabilities to handle uncertainty. For example, in navigation or tracking, they start with an initial guess and refine it as sensor data comes in, improving accuracy. This process is based on Bayes' theorem, which mathematically updates the likelihood of a hypothesis given new evidence, making Bayesian filters powerful tools for making informed predictions in dynamic, uncertain environments.