I am new to Bayesian statistics and am interested in the

**correlation of 4 "target" variables with 52 other variables**. Currently I am running several separate

**Bayesian Spearman rank correlations**. Many of the variables are

**highly correlated**(up to .68). Finally, I would like to report the correlation coefficients (effects of >= 0.1 will be considered practically relevant) and their 95% CIs (no Bayes Factors so far;

**just estimation**).

My (maybe not very smart) questions would be:

A)

**Do I have to apply multiple comparison correction?**Is this necessary, given that I am not engaging in hypothesis testing but estimation, rather?

B) In case the answer to A is "Yes, you do need to correct for multiple comparisons." - is there a

**Bayesian way of doing this**(ideally in R)?

In other questions related to this topic, the answer is usually that I would ideally just have one model in which I do not have to care about multiple comparison correction when comparing model parameters. However, this does not really match the situation I am in. I also see that I could engage in dimension reduction, but actually I would rather not (plus it wouldn't really help answering the question of whether, in principle, I need multiple comparison correction).

I would

**very much appreciate**any answer! Thank you!!