pyjpt
Joint Probability Trees - A formalism for learning and reasoning about joint probability distributions
Decision gist · record as of 2026-08-14
Yes, if you need interpretable probabilistic inference on hybrid data and can tolerate medium install friction. The package is actively maintained, has no known vulnerabilities, and offers a genuinely different approach to probabilistic modeling—tree-based partitioning rather than fixed dependency graphs. Verify the license terms first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8.
- Cython extensions are compiled during installation; set JPT_NO_CYTHON=1 to skip pre-compilation.
- Medium friction: 8 runtime dependencies including scipy, numpy, and pandas.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text provided. Verify the license terms before use in proprietary or copyleft-sensitive contexts.
last release 2026-06-01 (74 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 232,272 downloads/mo, #9,071 on PyPI
Alternatives
Verify before relying
pip install pyjpt
import pyjpt
# Create and train a JPT model on your data
# See documentation for full API usage- Whether the unclear license permits commercial use or requires attribution.
- Performance characteristics and scalability limits for large datasets.
- Availability of pre-built wheels for platforms beyond Linux x86_64.
- Maturity and stability guarantees relative to the research publication.
What it is and what it does
pyjpt implements Joint Probability Trees, a formalism for learning tractable probabilistic models from data. Unlike rigid dependency models, JPTs build tree structures that partition the probability space based on what the training data reveals, then use those partitions for inference. The package supports hybrid models mixing symbolic (discrete) and subsymbolic (continuous) variables without requiring prior knowledge of variable relationships.
The core value is interpretability: every inference result comes with white-box reasoning about how the tree structure produced that answer. Learning and inference scale linearly. The package wraps Cython extensions for performance and depends on scipy, numpy, pandas, anytree, tqdm, dnutils, deprecated, and typing-extensions for numerical and tree operations. Optional dependencies add plotting, sequential modeling, and MLflow integration.
Use it for
- Learn probabilistic models from mixed symbolic and continuous data without specifying a dependency structure upfront.
- Perform interpretable probabilistic inference where you need to explain why a posterior probability was computed.
- Build hybrid models combining discrete categories and continuous measurements in a single tractable framework.
- Analyze data where the relevant probability partitions are unknown and must be discovered from training examples.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need interpretable probabilistic inference on hybrid data and can tolerate medium install friction.
The package is actively maintained, has no known vulnerabilities, and offers a genuinely different approach to probabilistic modeling—tree-based partitioning rather than fixed dependency graphs. Verify the license terms first.
Install
pyjpt on PyPI
Before you install
Medium friction: 8 runtime dependencies including scipy, numpy, and pandas. Wheels available for multiple Python versions on Linux x86_64. Last release 74 days ago with active maintenance status.
Requires Python >=3.8. Cython extensions are compiled during installation; set JPT_NO_CYTHON=1 to skip pre-compilation.
License in practice
License treatment is unclear—no SPDX identifier or raw license text provided. Verify the license terms before use in proprietary or copyleft-sensitive contexts.
Quickstart
pip install pyjpt
import pyjpt
# Create and train a JPT model on your data
# See documentation for full API usageVerify before relying
- Whether the unclear license permits commercial use or requires attribution.
- Performance characteristics and scalability limits for large datasets.
- Availability of pre-built wheels for platforms beyond Linux x86_64.
- Maturity and stability guarantees relative to the research publication.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 8 packagesdnutilsscipynumpypandasdeprecatedtqdmanytreetyping-extensions |
| Maintenance | Actively maintained 74 days since the last release |
| First released | |
| Downloads | 232,272 / month, #9,071 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: pyjpt-1.3.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyjpt-1.3.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyjpt-1.3.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyjpt-1.3.4-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “joint probability distribution learning”
- pyjptpyjpt learns and reasons about joint probability distributions using…
- tfp-nightlyTensorFlow Probability provides probabilistic modeling, statistical…
- tensorflow-probabilityTensorFlow Probability provides probabilistic modeling, statistical…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also tensorflow-probability · pgmpy · tfp-nightly · problog · pymc-extras · pyro-ppl · numpyro · pyAgrum-nightly · py_trees · powerlaw