## Bayes’ Rule Applied

### Using Bayesian Inference on a real-world problem

The fundamental idea of Bayesian inference is to become “less wrong” with more data. The process is straightforward: we have an initial belief, known as a prior, which we update as we gain additional information. Although we don’t think about it as Bayesian Inference, we use this technique all the time. For example, we might initially think there is a 50% chance we will get a promotion at the end of the quarter. If we receive positive feedback from our manager, we adjust our estimate upwards, and conversely, we might decrease the probability if we make a mess with the coffee machine. As we continually gather information, we refine our estimate to get closer to the “true” answer.