After hours of videos and readings I am still having trouble understanding the fundamental conceptual differences between Bayesian and Frequentist probabilities. Some of the things I have read are contradictory, even from academics who seem like they know what they're talking about.

One of the main issues I am getting hung up on is priors. Frequentists begin with a prior data set of events to predict a result. If the data set expands, the prediction is updated to reflect the new data.

Bayesians begin with a different prior, and then use new data to update the prediction.

While the initial prediction comes from different priors, it seems like the results will converge with the addition of new data. The way the Bayesian approach is explained seems to indicate that the prior is updated and the prediction re-calculated, while suggesting that the frequentist approach is not updated with new data. What am I getting wrong?

I would appreciate any help in clarifying the difference between these two.

One of the main issues I am getting hung up on is priors. Frequentists begin with a prior data set of events to predict a result. If the data set expands, the prediction is updated to reflect the new data.

Bayesians begin with a different prior, and then use new data to update the prediction.

While the initial prediction comes from different priors, it seems like the results will converge with the addition of new data. The way the Bayesian approach is explained seems to indicate that the prior is updated and the prediction re-calculated, while suggesting that the frequentist approach is not updated with new data. What am I getting wrong?

I would appreciate any help in clarifying the difference between these two.

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