Hi everyone!

I am trying to model a customer lifetime value for a contractual discrete case as outlined here: Using MCMC to Fit the Shifted-Beta-Geometric Customer Lifetime Value Model . I can however not recreate this model in PyMC v4. Having read pm.DensityDist problem under PMYC 4 - #5 by zweli I have made sure all parameters are transferred. Here is my code and error. I was wondering if anyone could help:

```
import pymc as pm
import numpy as np
# ata prep
example_data = [1000, 869, 743, 653, 593, 551, 517, 491]
def n_lost(data):
lost = [None]
for i in range(1, len(data)):
lost.append(data[i - 1] - data[i])
return lost
example_data_n_lost = n_lost(example_data)
data = (example_data, example_data_n_lost)
num_periods = len(example_data)
active, lost = data
# bayesian model
with pm.Model() as bg_model:
alpha = pm.Uniform('alpha', 0.00001, 1000)
beta = pm.Uniform('beta', 0.00001, 1000)
def P_T_is_t(alpha=alpha, beta=beta, num_periods=num_periods):
p = [None, alpha / (alpha + beta)]
for t in range(2, num_periods):
pt = (beta + t - 2) / (alpha + beta + t - 1) * p[t-1]
p.append(pt)
return p
def survival_function(P_T_is_t=P_T_is_t, num_periods=num_periods):
s = [None, 1 - P_T_is_t[1]]
for t in range(2, num_periods):
s.append(s[t-1] - P_T_is_t[t])
return s
def logp(active, lost, alpha, beta, P_T_is_t=P_T_is_t, survival_function = survival_function):
# Those who've churned along the way...
died = np.log(P_T_is_t[1:]) * lost[1:]
# and those still active in last period
still_active = np.log(survival_function[-1]) * active[-1]
return sum(died) + still_active
# Custom distribution for BG likelihood function
loglikelihood = pm.DensityDist("loglikelihood", lost, alpha, beta, logp=logp, observed= active)
```

```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/Documents/__RN/rn_predictions/venv_pred/lib/python3.9/site-packages/aesara/tensor/type.py:278, in TensorType.dtype_specs(self)
277 try:
--> 278 return self.dtype_specs_map[self.dtype]
279 except KeyError:
KeyError: 'object'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
question_pymc.ipynb Cell 1 in <cell line: 21>()
return sum(died) + still_active
# Custom distribution for BG likelihood function
---> loglikelihood = pm.DensityDist("loglikelihood", lost, alpha, beta, logp=logp, observed= active)
File ~/Documents/__RN/rn_predictions/venv_pred/lib/python3.9/site-packages/pymc/distributions/distribution.py:753, in DensityDist.__new__(cls, name, logp, logcdf, random, moment, ndim_supp, ndims_params, dtype, *dist_params, **kwargs)
745 elif len(dist_params) > 0 and callable(dist_params[0]):
746 raise TypeError(
747 "The DensityDist API has changed, you are using the old API "
748 "where logp was the first positional argument. In the current API, "
(...)
751 "new DensityDist API."
752 )
...
--> 280 raise TypeError(
281 f"Unsupported dtype for {self.__class__.__name__}: {self.dtype}"
282 )
TypeError: Unsupported dtype for TensorType: object
```