# Using Custom blackbox likelihood and prior functions

**URL:** <https://discourse.pymc.io/t/using-custom-blackbox-likelihood-and-prior-functions/16957>\
**Category:** General\
**Created:** [April 29, 2025, 10:25pm UTC](https://discourse.pymc.io/t/using-custom-blackbox-likelihood-and-prior-functions/16957 "2025-04-29T22:25:48Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![lotten](https://avatars.discourse-cdn.com/v4/letter/l/e19adc/32.png) [@lotten](https://discourse.pymc.io/u/lotten)\
**Post date:** [April 29, 2025, 10:25pm UTC](https://discourse.pymc.io/t/using-custom-blackbox-likelihood-and-prior-functions/16957/1 "2025-04-29T22:25:48Z")

</div>

Hi,

I am looking to do a simple MCMC run to sample from a posterior distribution using 1000 samples extracted from a previous analysis as starting points. My posterior function (likelihood and posterior) uses numpy and scipy as well as if statements and since I also rely on scipy.root simply rewriting the function to work with aesara is not possible. I do of course realize that this will decrease performance, but I would like to implement this regardless. However, everything I have tried still throws errors such as “object of type ‘TensorVariable’ has no len()”, which indicates that my variables are still interpreted as tensor variables. Here is a short version of my current code:

with pm.Model() as model:  
# Define parameters  
x = pm.Flat(“x”, shape=(run.n\_dim,))

```
# Add log-likelihood and log-prior as potentials
pm.Potential("log_likelihood", run.log_like(x))
pm.Potential("log_prior", prior.logpdf(x)) # your log prior function

# Use Metropolis sampler
step = pm.Metropolis()

# Set up starting points from predefined 1000 samples
start_points = [{"x": pt} for pt in samples]

# Sample
trace = pm.sample(draws=1000, tune=500, step=step, chains=1000, cores=your_cores_count,
                  start=start_points, progressbar=True, discard_tuned_samples=True)

```

I have tried to wrap the functions using @as\_op(itypes=[at.dvector], otypes=[at.dscalar]) and nothing seems to work.

Is what I’m looking for even possible with pyMC?

---

<div class="post-metadata">

**Author:** ![ricardoV94](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ricardov94/32/5775_2.png) [@ricardoV94](https://discourse.pymc.io/u/ricardoV94)\
**Post date:** [April 30, 2025, 9:00am UTC](https://discourse.pymc.io/t/using-custom-blackbox-likelihood-and-prior-functions/16957/2 "2025-04-30T09:00:48Z")

</div>

The example notebook walks slowly over the steps to create your own op and test the implementation: [Using a “black box” likelihood function — PyMC example gallery](https://www.pymc.io/projects/examples/en/latest/howto/blackbox_external_likelihood_numpy.html)