# Getting rid of for loops for recursion of deterministic variables

I have the following code

``````import pandas as pd
import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
import arviz as az
import theano.tensor as tt
from scipy import optimize

seed = 68492
np.random.seed(seed)

basic_model = pm.Model()

with basic_model:

lambda1 = pm.Gamma("lambda1", alpha=0.001, beta=0.001)
pw =  pm.Beta('pw', 1.0, 1.0)
pu =  pm.Beta('pu', 1.0, 1.0)

mu = [1.0]*len(x['Length'])
Y_obs = [1.0]*len(x['Length'])

p = pm.Bernoulli("p", pw*x['Binary']+pu*(1-x['Binary']), shape=(len(x['Length'])) )
z = pm.Bernoulli("z", p, shape=(len(x['Length'])))

mu = pm.Deterministic('mu_0', lambda1*x['Length'].values
Y_obs = pm.Poisson("Y_obs_0", mu=(1-z)*mu+0.0001, observed=x['Count'].values)#, shape=len(x['Length']))

for i in range(1,len(x['Length'])):
if x['Length'].values[i-1] == x['Length'].values[i]:
mu[i] = pm.Deterministic('mu_%i' % i, mu[i-1]*z[i-1] + lambda1*x['Length'].values[i])
else:
mu[i] = pm.Deterministic('mu_%i' % i, lambda1*x['Length'].values[i]

Y_obs[i] = pm.Poisson('Y_obs_%i' % i,
mu = (1-z[i])*mu[i] + 0.0001,
observed = x['Count'].values[i] )

trace = pm.sample(7000, tune=2000, cores=1, return_inferencedata=True)

print(az.summary(trace, kind="stats"))

print(az.rhat(trace, var_names=['lambda1', 'pw', 'pu'], method="folded"))

``````

`mu[i]` is the mean for the i^th data point, and it depends on `z[i-1]` and `mu[i-1]`. This way, the data size is very large, so I get the following error:
`bracket nesting level exceeded maximum of 256`

It works for smaller data, though.
How do I break the recursion for `mu` and `Y_obs` free from the for loop?

Edit: I tried this for `Y_obs`, but I don’t know how to get it for `mu`.

`Y_obs = pm.Poisson('Y_obs', mu = ((1-z) * mu).sum(axis=1) + 0.0001, observed = x['Count'].values, shape=len(x['Length']) )`