Edward

What it is

Edward is a Python library for probabilistic modeling, inference, and criticism. It builds on top of TensorFlow to enable flexible and scalable probabilistic models.

Edward stands out as a specialized library for probabilistic modeling, offering flexibility and scalability through its integration with TensorFlow.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Steep learning curve for non-statisticians and machine learning experts

   Edward's probabilistic modeling requires a strong understanding of Bayesian statistics and TensorFlow

2. 02

   Limited documentation and examples

   The official documentation lacks comprehensive tutorials and practical examples for various use cases

3. 03

   Performance issues with complex models

   Edward can suffer from slow inference times when dealing with large datasets or intricate probabilistic models

4. 04

   Dependency on TensorFlow's ecosystem and limitations

   As Edward is built on top of TensorFlow, it inherits any performance bottlenecks or API changes that affect TensorFlow users

What it does well

• Probabilistic modeling and inference

• Built on TensorFlow for scalability

• Supports a wide range of probabilistic models

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.