Update dims_module notebook and added links to distributions, transfo…

[vjhanani]

…rms, and math docs

Description

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• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
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• New feature / enhancement
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• Documentation
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Documentation build overview

📚 pymc | 🛠️ Build #31819602 | 📁 Comparing 08c63e0 against latest (f89e6c8)

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Show files changed (20 files in total): 📝 20 modified | ➕ 0 added | ➖ 0 deleted

File	Status
glossary.html	📝 modified
api/dims.html	📝 modified
api/generated/pymc.draw.html	📝 modified
api/generated/pymc.icdf.html	📝 modified
api/generated/pymc.logcdf.html	📝 modified
api/generated/pymc.logp.html	📝 modified
api/generated/pymc.sample_posterior_predictive.html	📝 modified
api/generated/pymc.sample_prior_predictive.html	📝 modified
learn/core_notebooks/GLM_linear.html	📝 modified
learn/core_notebooks/dims_module.html	📝 modified
learn/core_notebooks/index.html	📝 modified
learn/core_notebooks/pymc_pytensor.html	📝 modified
_modules/pymc/distributions/distribution.html	📝 modified
_modules/pymc/distributions/multivariate.html	📝 modified
_modules/pymc/logprob/basic.html	📝 modified
_modules/pymc/model/core.html	📝 modified
_modules/pymc/sampling/forward.html	📝 modified
_modules/pymc/step_methods/metropolis.html	📝 modified
api/distributions/generated/classmethods/pymc.SymbolicRandomVariable.update.html	📝 modified
api/model/generated/classmethods/pymc.model.core.Model.compile_fn.html	📝 modified

[vjhanani]

Hello @fonnesbeck,
I am interested in contributing to the Streaming Inference and Minibatch Extension project in PyMC for GSoC 2026.

I have started exploring the repository and recently opened a pull request for a documentation fix to get familiar with the contribution workflow and codebase.

From the project description, I understand that the goal is to extend the current minibatch functionality to support streaming data and make it compatible with multiple inference methods such as variational inference and Pathfinder. I find this particularly interesting, especially for scaling inference to large datasets, potentially integrating with tools like Dask.

I would really appreciate your guidance on:

• Whether I am heading in the right direction with my current contributions
• Which parts of the codebase I should focus on next to move toward core contributions
• Any beginner-friendly issues related to minibatch or inference that I can work on
• Any suggestions on how to approach and structure a strong GSoC proposal for this project

Thank you for your time. I look forward to contributing further.