http://www.people.fas.harvard.edu/~plam/teaching/methods/mcmc/mcmc.pdf

on page 175. It is written in R but I am trying to write it in Python but running into problems.

Here is my python code

Code:

```
from numpy import *
import matplotlib.pylab as pl
def gibbs_sampler(alpha,delta,gamma,y,t):
#initialize beta
beta=1
num_iter=100
beta_draws=[]
lambda_draws=[]
for i in range(num_iter):
#sample lambda given other lambdas and beta
lambdas=lambda_update(alpha,beta,y,t)
#record sample
lambda_draws.append(lambdas)
#sample beta given lambda samples
beta=beta_update(alpha,gamma,delta,lambdas,y)
#record sample
beta_draws.append(beta)
pl.plot(array(beta_draws))
pl.show()
def lambda_update(alpha,beta,y,t):
new_alpha=[(x+alpha) for x in y]
new_beta=[(a+beta) for a in t]
#sample from this distribution 10 times
samples=random.gamma(new_alpha,new_beta)
return samples
def beta_update(alpha,gamma,delta,lambdas,y):
#get sample
sample=random.gamma(len(y)*alpha+gamma,delta+sum(lambdas))
return sample
def main():
y=[5,1,5,14,3,19,1,1,4,22]
t=[94,16,63,126,5,31,1,1,2,10]
alpha=1.8
gamma=0.01
delta=1
gibbs_sampler(alpha,delta,gamma,y,t)
if __name__ == '__main__':
main()
```

Thanks