How Does Prior Probability Influence Bayesian Inference?
In Bayesian inference, the prior probability is combined with new data (likelihood) to form the posterior probability. This updated probability reflects the likelihood of an event or condition given the new evidence. The process can be repeated as more data becomes available, continually refining the estimates. This iterative approach allows for a dynamic and responsive analysis, which is particularly valuable in rapidly changing public health scenarios.
