Bayesian thinking involves putting prior knowledge into the prediction.
For a very straightforward example, assume you want to know (compute) the probability that someone is a male or a female. From a frequentist perspective, before the pesron reveals its gender, there is 50% chance that this person is a male and 50% a female.
From a bayesian perspective we can add some biasis (prior) to perform a better prediction. In the example above, our knowledge about what distinguish a male from a female (hair, voice, ...) and the actual observation can be used to perform more accurate prediction, before the person reveals its true gender.
Bayesian thinking involves putting prior knowledge into the prediction.
For a very straightforward example, assume you want to know (compute) the probability that someone is a male or a female. From a frequentist perspective, before the pesron reveals its gender, there is 50% chance that this person is a male and 50% a female.
From a bayesian perspective we can add some biasis (prior) to perform a better prediction. In the example above, our knowledge about what distinguish a male from a female (hair, voice, ...) and the actual observation can be used to perform more accurate prediction, before the person reveals its true gender.