# What frequentist statistics topics should I know before learning Bayesian statistics?

I was wondering if there is a subset of topics of frequentist statistics that one should know before starting to learn Bayesian statistics. Once I read that it seems that the two trends are antagonistic to each other; like for example frequentist analysis is based heavily on assumptions (hypothesis) that are made over observed data; while Bayesian statistics rely more in the construction of a prior model to infer posterior information about it.

In any case, which topics of frequentist or general statistics should I know before embarking upon Bayesian statistics?

It is not necessary to call it frequentist material, rather material from probability and statistics in general.

Here are some examples of prior knowledge that, in my opinion, would be handy:

1. What are densities, (conditional) distributions, expectations etc.?
2. Some specific distributional families (Beta, normal, uniform etc.)
3. Most likely you will want to apply Bayesian methods to real data, so
statistical software. My favorite: R
4. Some mathematics: Matrix algebra, integration, …
5. Also, it could be handy to be familiar with some statistical models, such as the linear model $y=X\beta+u$.
6. Given the heavy emphasis on the likelihood, it cannot hurt to have heard about maximum likelihood before

The Bayesian paradigm being a subjective one, I am sure others will disagree with or add to this list…