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Answer by darko-10

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.