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Fix typo (#656)
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examples/howto/blackbox_external_likelihood_numpy.ipynb

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examples/howto/blackbox_external_likelihood_numpy.myst.md

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@@ -64,7 +64,7 @@ Another issue is that if you want to be able to use the gradient-based step samp
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Defining a model/likelihood that PyMC can use and that calls your "black box" function is possible, but it relies on creating a custom PyTensor Op. This is, hopefully, a clear description of how to do this, including one way of writing a gradient function that could be generally applicable.
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In the examples below, we create a very simple lineral model and log-likelihood function in numpy.
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In the examples below, we create a very simple linear model and log-likelihood function in numpy.
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```{code-cell} ipython3
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def my_model(m, c, x):

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