pytensor-distributions
PyTensor powered distributions.
Decision gist · record as of 2026-08-14
Yes, if you are working with PyMC or building probabilistic models that benefit from PyTensor's symbolic computation. The low install friction, permissive license, and active maintenance make it a reasonable choice. However, the alpha status means the API and feature coverage may change; verify that the distributions and expressions you need are already implemented before committing to a production dependency.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; pytensor must be installed and functional.
- Low install friction; the package is a pure Python wheel with only numpy and pytensor as runtime dependencies.
- Actively maintained with a recent release, though still in alpha development status.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-06-08 (67 days) · last repo commit 2026-06-08 · 5 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 248,129 downloads/mo, #8,675 on PyPI
Alternatives
Verify before relying
pip install pytensor-distributions
import pytensor_distributions as ptd
import numpy as np
# Example: compute pdf of a distribution
pdf_value = ptd.normal.pdf(x=0.0, mu=0.0, sigma=1.0)- Which specific distributions are currently implemented and which core expressions each supports
- Performance characteristics compared to scipy or other probability libraries
- API stability guarantees given the alpha development status
What it is and what it does
PyTensor-distributions is a library of probability distribution functions and statistical properties implemented as PyTensor symbolic expressions. It provides a unified, tested source for core probability operations—pdf, cdf, ppf, logpdf, mean, variance, entropy, and others—designed to reduce redundancy across the PyMC ecosystem and allow other libraries to build specialized APIs on top of these expressions.
The package takes a minimal, function-based design approach: one file per distribution, no classes, and a focus on simplicity. It depends only on numpy and pytensor, making it lightweight to install. It is currently in active development (alpha status) and welcomes community contributions.
Use it for
- Building probabilistic models in PyMC or other Bayesian inference frameworks that need standardized distribution expressions
- Computing probability density, cumulative distribution, and inverse functions for statistical analysis workflows
- Implementing custom probabilistic algorithms that require symbolic distribution operations via PyTensor
- Reducing code duplication by using a shared, tested library of distribution implementations across multiple projects
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with PyMC or building probabilistic models that benefit from PyTensor's symbolic computation.
The low install friction, permissive license, and active maintenance make it a reasonable choice. However, the alpha status means the API and feature coverage may change; verify that the distributions and expressions you need are already implemented before committing to a production dependency.
Install
pytensor-distributions on PyPI
Before you install
Low install friction; the package is a pure Python wheel with only numpy and pytensor as runtime dependencies. Actively maintained with a recent release, though still in alpha development status.
Requires Python 3.12 or later; pytensor must be installed and functional.
License in practice
Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install pytensor-distributions
import pytensor_distributions as ptd
import numpy as np
# Example: compute pdf of a distribution
pdf_value = ptd.normal.pdf(x=0.0, mu=0.0, sigma=1.0)Verify before relying
- Which specific distributions are currently implemented and which core expressions each supports
- Performance characteristics compared to scipy or other probability libraries
- API stability guarantees given the alpha development status
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpypytensor |
| Maintenance | Actively maintained 67 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 248,129 / month, #8,675 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: pytensor_distributions-0.2.0-py3-none-any.whl
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