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?

**Answer**

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:

- What are densities, (conditional) distributions, expectations etc.?
- Some specific distributional families (Beta, normal, uniform etc.)
- Most likely you will want to apply Bayesian methods to real data, so

statistical software. My favorite: R - Some mathematics: Matrix algebra, integration, …
- Also, it could be handy to be familiar with some statistical models, such as the linear model y=Xβ+u.
- 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…

**Attribution***Source : Link , Question Author : Community , Answer Author :
Christoph Hanck
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