Hello,

This may be a silly question, but I’m quite new to this software and can’t get around this issue.

I’m trying to set up an mcmc code for which I need a custom likelihood, that is defined as a complicated external function, that involves ffts, integrals, etc. As I understood, the best choice is to define a class based on a Theano Op. I’m now following the “Black box” example for a Gaussian distribution, just to be sure that everything is set up. But it’s not working. This is my code, it is really simple:

```
alpha, sigma = 5, 1
size = 10000
# Simulate outcome variable
Y = alpha + np.random.randn(size)*sigma
def my_loglike(theta):
alpha,sigma = theta
return -(0.5/sigma**2)*np.sum((alpha - Y)**2)
class LogLike(tt.Op):
itypes = [tt.dvector] # expects a vector of parameter values when called
otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)
def __init__(self, loglike):
self.likelihood = loglike
def perform(self, node, inputs, outputs):
# the method that is used when calling the Op
theta, = inputs # this will contain my variables
# call the log-likelihood function
logl = self.likelihood(theta)
outputs[0][0] = np.array(logl) # output the log-likelihood
loglik = LogLike(my_loglike)
with pm.Model() as model:
alpha = pm.Uniform('alpha', 0, 25)
sigma = pm.Uniform('sigma', 0.1, 2)
# Create likelihood
theta = tt.as_tensor_variable([alpha, sigma])
# use a DensityDist (use a lamdba function to "call" the Op)
pm.DensityDist('likelihood', lambda v: loglik(v), observed={'v': theta})
trace = pm.sample(1000)
```

The error that I’m getting is the following. Looking for it on the internet, I haven’t found anything useful

`MissingInputError: Input 0 of the graph (indices start from 0), used to compute sigmoid(sigma_interval__), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error.`