# How to interpret estimates and correlation of random effects (intercepts and slope) in a mixed-effects model in a Bayesian framework(brms)?

I do not understand how to interpret random slopes from the output of brms

Among others, I read this post on the output from lmer. However instead of Variance in brms I have Estimate, as for fixed factor!!. This page also helped me a bit.

In the following example the variable "experience.Mom" has a positive estimate at the group level (random slope) which, if I understand correctly is always the case (why??), and a negative effect at the population level. (intervals are over the 0 so no clear effect anyhow).

What does my random slope Estimate means? How does it influence my outcome variable (Y)?

# Group level (random)

                                              Estimate Est.Error l-95% CI u-95% CI Eff.Sample
sd(Intercept)                                     0.08      0.06     0.00     0.23       4323
sd(experience_Mom.z)                              0.09      0.06     0.00     0.24       4831
cor(Intercept,experience_Mom.z)                   0.00      0.57    -0.94     0.95       7528


# Population level (fixed)

                                                            Estimate Est.Error l-95% CI

experience_Mom.z                                               -0.02      0.09    -0.20


In the output from brms you have posted the column Estimate gives you the estimates of the standard deviation of the random intercepts, the standard deviation of the random slopes, and the correlation between the intercepts and slopes. This corresponds to the second and third columns of the output you obtain from lmer() of lme4 named Std.Dev. and Corr.