Comments (2)
I find it extremely useful to generate a DAG for my model before I run it:
from pymc import graph
graph.dag(my_model)
This will show visually if there are any orphan nodes in your model.
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Why not check the name argument. See commit f733d06.
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Related Issues (20)
- Test `freeze_rv_and_dims` with `PartiallyObservedRV` HOT 1
- ENH: Customizable plate labels HOT 11
- First time congratulatory message images are huge HOT 8
- DOC: Plots squished in the documentation page HOT 2
- Problematic casting of integers in `convert_observed_data`
- ENH: Get sampling working using Apple Silicon GPU via jax backend HOT 32
- DOC: conflicting `fixed_params` type-hint in `find_constrained_prior` HOT 1
- ENH: Support foward sampling via JAX HOT 7
- ConnectionResetError from multiprocess sampling
- BUG: Repeated predictions from GP grow steadily slower (pytensor cache issue?) HOT 1
- BUG: Compiler error when calling pm.sample() HOT 2
- Remove `mutable=True` in test suite HOT 1
- Numpy deprecation warning issued from within `test_metropolis.py`
- ENH: Add an explicit dummy prior for predictive modeling HOT 1
- Add CustomDist as second level header below Distributions in the API HOT 1
- ENH: implement deterministic ADVI (DADVI)
- BUG: Using a length-only coord causes it to be volatile HOT 5
- ENH: Incorporated a download analytics badge that tracks all downloads from Conda. HOT 1
- RTD is broken HOT 3
- Missing static information for jax backend for models with missing values HOT 3
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