```
# import theano
from pymc3.math import exp, log
class ParetoNBD(pm.Continuous):
"""
Custom distribution class for Pareto/NBD likelihood.
"""
def __init__(self, lambda_, mu, *args, **kwargs):
super(ParetoNBD, self).__init__(*args, **kwargs)
self.lambda_ = lambda_
self.mu = mu
def logp(self, x, t_x, T):
"""
Loglikelihood function for and indvidual customer's purchasing rate \lambda
and lifetime \mu given their frequency, recency and time since first purchase.
"""
log_lambda = log(self.lambda_)
log_mu = log(self.mu)
mu_plus_lambda = self.lambda_ + self.mu
log_mu_plus_lambda = log(mu_plus_lambda)
p_1 = x * log_lambda + log_mu - log_mu_plus_lambda - t_x * mu_plus_lambda
p_2 = (x + 1) * log_lambda - log_mu_plus_lambda - T * mu_plus_lambda
return log(exp(p_1) + exp(p_2))
```

This is a custom distribution I have been using from this notebook.

https://github.com/benvandyke/pydata-seattle-2017/blob/master/lifetime-value/pareto-nbd.ipynb

I am running into issues when I try to run this piece of work with a different dataset. I have some nanâ€™s in `mu`

when sampling and that is causing the following errors.

```
The derivative of RV `lambda_log__`.ravel()[5921] is zero.
The derivative of RV `lambda_log__`.ravel()[5922] is zero.
The derivative of RV `beta_log__`.ravel()[0] is zero.
The derivative of RV `s_log__`.ravel()[0] is zero.
The derivative of RV `alpha_log__`.ravel()[0] is zero.
The derivative of RV `r_log__`.ravel()[0] is zero.
```

A couple of questions:

- How can I debug why some of my mu values are nan? and is there a way to overcome that?
- Is there a way for me to use logsumexp for
`log(exp(p_1) + exp(p_2))`

?

Help is greatly appreciated!!