Hi @ccaprani ,

Again here for some queries. Could start a new thread if you wish.

So it was decided to put on hold the incorporation of GEV to v.4. I checked PYMC experimental, but it’s missing there as well.

I was trying to run the custom distribution in v.4 but get some errors. Here is my code:

```
def gev_logp(value,μ , σ, ξ):
scaled = (value - μ ) / σ
logp_xi_not_zero = -(at.log( σ)
+ (( ξ + 1) / ξ) * at.log1p( ξ * scaled)
+ (1 + ξ * scaled) ** (-1/ ξ))
logp_xi_zero = -at.log( σ) + ( ξ+1)*(-(value - μ )/ σ) - at.exp(-(value - μ )/ σ)
logp = at.switch(at.abs_( ξ) > 1e-4 , logp_xi_not_zero, logp_xi_zero)
return at.sum(logp)
with pm.Model() as model:
μ = pm.Normal("μ", mu=1, sigma=100)
σ= pm.Normal("σ",mu=1, sigma=100)
ξ= pm.Beta("ξ",alpha=6, beta=9)
gev = pm.DensityDist( "gev", logp= gev_logp, observed = {'value':data, 'μ':μ, 'σ':σ, 'ξ':ξ})
idata = pm.sample(1000, tune=1500)
```

```
TypeError: float() argument must be a string or a real number, not 'dict'
```

I guess there might be some issues with the aesara tensor here?