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!