Please also provide the following information in addition to your question:

- Operating System: MacOS 10.14.5
- brms Version: 2.7.0

I have a complex multivariate hierarchical model with very few data (2 points per cluster). So I need to define very strong priors to let the model be fitted robustly (and in reasonable time). But since the model is complex there are a lot of priors to set.

Is there a way to set many at once?

And then, is there a way to check the prior setting before fitting the model? get_prior only let you see default priors and to use prior_summary you need to fit the model first (-> hours).

Here’s my model for example:

```
> get_prior(bf(N.prest ~ Mesi + (Mesi|Context), family = poisson()) + bf(Attesa ~ log(N.prest) + scale(Mesi) + (log(N.prest) + scale(Mesi)|Context), sigma ~ log(N.prest) + scale(Mesi) + (log(N.prest) + scale(Mesi)|Context), family = lognormal()), data = TdA, set_prior('normal(0, 2.5)', class = 'sd', group = 'Context'))
Setting 'rescor' to FALSE by default for this model
prior class coef group resp dpar nlpar bound
1 b
2 lkj(1) cor
3 cor Context
4 Intercept
5 b Attesa
6 b logN.prest Attesa
7 b scaleMesi Attesa
8 student_t(3, 3, 10) Intercept Attesa
9 student_t(3, 0, 10) sd Attesa
10 sd Context Attesa
11 sd Intercept Context Attesa
12 sd logN.prest Context Attesa
13 sd scaleMesi Context Attesa
14 b Attesa sigma
15 b logN.prest Attesa sigma
16 b scaleMesi Attesa sigma
17 student_t(3, 0, 10) Intercept Attesa sigma
18 student_t(3, 0, 10) sd Attesa sigma
19 sd Context Attesa sigma
20 sd Intercept Context Attesa sigma
21 sd logN.prest Context Attesa sigma
22 sd scaleMesi Context Attesa sigma
23 b Nprest
24 b Mesi Nprest
25 student_t(3, 4, 10) Intercept Nprest
26 student_t(3, 0, 10) sd Nprest
27 sd Context Nprest
28 sd Intercept Context Nprest
29 sd Mesi Context Nprest
```

For example, I would like to be able to set a prior for all Context sd at once, for all coef, resp and dpar.