Fix target in implementing_distribution.md

docs/source/contributing/developer_guide.md

@@ -14,7 +14,7 @@ A more accessible, user facing deep introduction can be found in [Peadar Coyle's
 Probability distributions in PyMC are implemented as classes that inherit from {class}`~pymc.Continuous` or {class}`~pymc.Discrete`.
 Either of these inherit {class}`~pymc.Distribution` which defines the high level API.
 
-For a detailed introduction on how a new distribution should be implemented check out the {ref}`guide on implementing distributions <developer_guide_implementing_distribution>`.
+For a detailed introduction on how a new distribution should be implemented check out the {ref}`guide on implementing distributions <implementing_distribution>`.
 
 
 ## Reflection

docs/source/contributing/implementing_distribution.md

@@ -88,7 +88,7 @@ blah = BlahRV()
 Some important things to keep in mind:
 
 1. Everything inside the `rng_fn` method is pure Python code (as are the inputs) and should not make use of other `Aesara` symbolic ops. The random method should make use of the `rng` which is a NumPy {class}`~numpy.random.RandomState`, so that samples are reproducible.
-1. Non-default `RandomVariable` dimensions will end up in the `rng_fn` via the `size` kwarg. The `rng_fn` will have to take this into consideration for correct output. `size` is the specification used by NumPy and SciPy and works like PyMC `shape` for univariate distributions, but is different for multivariate distributions. For multivariate distributions the __`size` excludes the `ndim_supp` support dimensions__, whereas the __`shape` of the resulting `TensorVariabe` or `ndarray` includes the support dimensions__. For more context check {doc}`The dimensionality notebook <pymc:dimensionality=>`.
+1. Non-default `RandomVariable` dimensions will end up in the `rng_fn` via the `size` kwarg. The `rng_fn` will have to take this into consideration for correct output. `size` is the specification used by NumPy and SciPy and works like PyMC `shape` for univariate distributions, but is different for multivariate distributions. For multivariate distributions the __`size` excludes the `ndim_supp` support dimensions__, whereas the __`shape` of the resulting `TensorVariabe` or `ndarray` includes the support dimensions__. For more context check {ref}`The dimensionality notebook <dimensionality>`.
 1. `Aesara` tries to infer the output shape of the `RandomVariable` (given a user-specified size) by introspection of the `ndim_supp` and `ndim_params` attributes. However, the default method may not work for more complex distributions. In that case, custom `_supp_shape_from_params` (and less probably, `_infer_shape`) should also be implemented in the new `RandomVariable` class. One simple example is seen in the {class}`~pymc.DirichletMultinomialRV` where it was necessary to specify the `rep_param_idx` so that the `default_supp_shape_from_params` helper method can do its job. In more complex cases, it may not suffice to use this default helper. This could happen for instance if the argument values determined the support shape of the distribution, as happens in the `~pymc.distributions.multivarite._LKJCholeskyCovRV`.
 1. It's okay to use the `rng_fn` `classmethods` of other Aesara and PyMC `RandomVariables` inside the new `rng_fn`. For example if you are implementing a negative HalfNormal `RandomVariable`, your `rng_fn` can simply return `- halfnormal.rng_fn(rng, scale, size)`.
 

docs/source/contributing/index.md

@@ -90,7 +90,7 @@ pr_tutorial
 :maxdepth: 1
 :caption: How-to guides
 
-developer_guide_implementing_distribution
+implementing_distribution
 build_docs
 running_the_test_suite
 review_pr_pymc_examples