I have data that I want to model using a `Weibull`

(or actually Rayleigh) distribution. I want to model it such that the beta parameter changes “over time” (data is a time series).

I tried to model that using a `GaussianRandomWalk`

, see here:https://github.com/pymc-devs/pymc3/issues/3584

```
with pm.Model() as model:
beta = pm.SomeThing('beta') # <-- some positive-only time-series model here
y_obs = pm.Weibull('y_obs',
alpha=2, #Rayleigh distribution
beta=beta,
observed=d)
```

That of course does not work, since the beta parameter is required to be positive, while the `GaussianRandomWalk`

produces any kind of real value (thus also negative values). Do you have any suggestions how this could be modeled?

I have already tried to ignore time in my first approach and do analysis on distinct windows of the data, but now I want to integrate the time domain, as my visualizations suggest that this might make sense.

Thanks in advance for any ideas!