Bayesian probabilities are different from frequentist probabilities. An abridged summary: Frequentist probability is about the limit that something approaches

Bayesian probabilities are different from frequentist probabilities. An abridged summary: Frequentist probability is about the limit that something approaches after many repetitions. e.g. rolling a dice. It works well in a casino and some quantum physics applications but mostly fails outside of that. e.g. we want to know what the probability of a team winning the next superbowl is, or what is the probability of life on Mars. A frequentist would say that question doesn't make sense, there's either life on mars or there isn't, there's going to only be one result of the next superbowl. Bayesian probability is about the degree of belief. It is about how to update the degree of belief based on prior knowledge and observations. e.g. If we thought there was life on mars, we should see some evidence of it, we don't see evidence so we should adjust our prior belief of the probability of life on mars downward. It answers the questions we want to ask, but those answers frequently depend on what priors you choose, and if those priors can sometimes dominate the analysis.