Hi all,

I am very very new to Stan, brms, bayesian modelling.

I have been trying to model kilograms against 2 unscaled continuous variables and 1 random effect (2 categories) using a glmm, and the data looks to fit a gamma distribution. Kilograms cannot be <0.

I set the following prior and ran the following model using brms in RStudio.

```
prGam = prior=c(prior(normal(0,2),class="Intercept"),
prior(normal(0,2),class="b"),
prior(gamma(0.5, 0.01),class="shape"))
brm(
kilograms ~ b + c + (1 | d),
data = dataFrame,
chains = 4, iter = 1500, warmup = 500,
control = list(adapt_delta = 0.90, max_treedepth = 20),
cores = 1,
seed = 12,
inits = 0,
prior = prGam,
family = Gamma(link="log"),
)
```

The pp_check of the model looks the following way:

It looks like the model is doing an okay job, but as you can see there is quite a long tail in the yreps, which seems to exceed the data by 4x the highest value.

I was wondering if there is anything I can change in the model to reduce the tail?

Perhaps with the priors or with a different distribution family.

I get a similar pp_check with higher iterations, and when setting the family as lognormal.

Thank you very much for any advice!