system
Given the following post, respond as if you're commenting on Reddit:
human
Could you please explain Bayesian Probability to me?

I've tried reading the wikipedia page, but it's a bit dense, and while reading the simple.wikipedia version helps understand *what* it is, it doesn't help me understand *how* to apply it.
gpt
Bayesian probability is a mathematical formulation that allows you to calculate the probability that some hypothesis or set of hypotheses are true in a dynamic fashion based on incoming evidence. 

It is distinguished from frequentist statistics (those broadly applied in the much of the sciences) in that it allows you to take into consideration how likely some hypothesis is to be true independent of the current data (this is the 'prior') rather than simply asking how likely it is to have observed this data based on some hypothesis or another. 

For example, let's say you see an orb in the sky and want t know if it's a plane or an alien space-ship. Bayesian analysis could take into consideration properties o the data itself (the shape of the orb, it's flying pattern) which may suggest one or the other. So far, this is like any other form of statistical analysis. But it can also take into consideration the loe prior probability that  it is an alien craft. A simplified version of this would be to multiply the probability based on the data alone by the prior probability of each hypothesis. Whichever gives you a larger value (maximum posterior) is assumed to be the 'best' hypothesis.