pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.
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Edward provides a library for probabilistic modeling, inference, and model criticism.
About Edward
Edward is a Python library for probabilistic modeling, inference, and criticism. It is built on TensorFlow and combines ideas from Bayesian statistics and machine learning, deep learning, and probabilistic programming. It is described as a testbed for experimentation and research with probabilistic models.
With Edward, you can build models ranging from classical hierarchical models on small data sets to deep probabilistic models on large data sets. Supported modeling approaches include directed graphical models, neural networks via libraries such as tf.layers and Keras, implicit generative models, and Bayesian nonparametrics. For inference, it lists methods such as variational inference, Monte Carlo, Hamiltonian Monte Carlo, and expectation-maximization, among others. It also supports model criticism through point-based evaluations and posterior predictive checks.
The site provides getting started guides, tutorials, API documentation, and community and contributing information. It suits researchers and developers working with probabilistic models who use Python and TensorFlow.
Key features
- Probabilistic modeling, inference, and criticism in Python
- Support for directed graphical models and probabilistic programs
- Neural network modeling via tf.layers and Keras
- Inference methods including variational inference and Hamiltonian Monte Carlo
- Model criticism with posterior predictive checks
- Built on TensorFlow with TensorBoard visualization
Best for: Researchers and developers building and testing probabilistic models in Python with TensorFlow.
Good for
- Covers modeling, inference, and criticism in one library
- Supports small hierarchical models to large deep probabilistic models
- Provides tutorials, API reference, and getting started guides
- Built on TensorFlow for distributed training and GPU use
Watch out for
- Requires Python and TensorFlow to use
Edward at a glance
- Website
- edwardlib.org
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Edward FAQ
What is Edward?
Edward is a Python library for probabilistic modeling, inference, and criticism built on TensorFlow.
What can I model with Edward?
You can work with directed graphical models, neural networks, implicit generative models, and Bayesian nonparametrics.
What inference methods does Edward support?
It lists variational inference, Monte Carlo methods, Hamiltonian Monte Carlo, expectation-maximization, and other methods.
Who develops Edward?
Edward is led by Dustin Tran with guidance by David Blei, with contributions from researchers and developers.
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