The difference between Bayesian and frequentist inference in a nutshell:
With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process may be), to quantify evidence for every possible value of θ. With frequentism, you make assumptions about the process that generated your data, and try to build evidence for what θ is not.
Imagine watching a baseball game, seeing the batter get a hit, and hearing the announcer say “The chance that the batter is left handed is now 0.2!”
No one would care. Baseball fans are interested in the chance that a batter will get a hit conditional on his being right handed (handedness being already known to the fan), the handedness of the pitcher, etc. Unless one is an archaeologist or medical examiner, the interest is in forward probabilities conditional on current and past states.