# Computing fitted values for Weibull using posterior samples?

**URL:** <https://discourse.mc-stan.org/t/computing-fitted-values-for-weibull-using-posterior-samples/20422>\
**Category:** brms\
**Created:** [January 25, 2021, 5:08pm UTC](https://discourse.mc-stan.org/t/computing-fitted-values-for-weibull-using-posterior-samples/20422 "2021-01-25T17:08:56Z")\
**Posts on this page:** 1\
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**Author:** ![torkar](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/torkar/32/1355_2.png) [@torkar](https://discourse.mc-stan.org/u/torkar)\
**Post date:** [January 31, 2021, 2:04pm UTC](https://discourse.mc-stan.org/t/computing-fitted-values-for-weibull-using-posterior-samples/20422/2 "2021-01-31T14:04:33Z")

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I think @jonah’s post clarifies the common ways to do posterior draws in `brms`, does this help you?

> [@Reproduce posterior\_predict from posterior\_linpred for a neg binom spline model](https://discourse.mc-stan.org/t/reproduce-posterior-predict-from-posterior-linpred-for-a-neg-binom-spline-model/18835/2):
>
> Hi Angelo, here’s a brief explanation of the difference. Suppose you have a negative binomial regression with a log link: \eta\_i = a + bx\_i \\ \lambda\_i = \exp(\eta\_i) \\ y\_i \sim {\rm NegBinom}(\lambda\_i, \phi) posterior\_linpred() gives you posterior draws of \eta. posterior\_linpred(transform=TRUE), or equivalently posterior\_epred(), gives you posterior draws of \lambda, that is, it does the inverse link transformation for you. posterior\_predict() gives you draws from {\rm NegBinom}(\l…

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