Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/blei-lab/edward
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
https://github.com/blei-lab/edward
bayesian-methods data-science deep-learning machine-learning neural-networks probabilistic-programming statistics tensorflow
Last synced: 3 days ago
JSON representation
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
- Host: GitHub
- URL: https://github.com/blei-lab/edward
- Owner: blei-lab
- License: other
- Created: 2016-02-10T20:06:05.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-03-18T16:23:03.000Z (10 months ago)
- Last Synced: 2025-01-02T03:09:06.667Z (10 days ago)
- Topics: bayesian-methods, data-science, deep-learning, machine-learning, neural-networks, probabilistic-programming, statistics, tensorflow
- Language: Jupyter Notebook
- Homepage: http://edwardlib.org
- Size: 28.5 MB
- Stars: 4,831
- Watchers: 272
- Forks: 758
- Open Issues: 220
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-python-machine-learning - Edward - Edward is a Python library for probabilistic modeling, inference, and criticism. (Uncategorized / Uncategorized)
- awesome-topic-models - edward - A PPL built on TensorFlow, e.g., [LDA](http://edwardlib.org/iclr2017?Figure%2011.%20Latent%20Dirichlet%20allocation) [:page_facing_up:](https://arxiv.org/pdf/1610.09787.pdf) (Probabilistic Programming Languages (PPL) (a.k.a. Build your own Topic Model) / Embedding based Topic Models)
- awesome-list - Edward - A probabilistic programming language in TensorFlow. Deep generative models, variational inference. (Linear Algebra / Statistics Toolkit / Statistical Toolkit)
- awesome-python-machine-learning-resources - GitHub - 36% open · ⏱️ 25.07.2018): (概率统计)
README
[](http://edwardlib.org)
[](https://travis-ci.org/blei-lab/edward)
[](https://coveralls.io/github/blei-lab/edward?branch=master)
[](https://gitter.im/blei-lab/edward?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[Edward](http://edwardlib.org) is a Python library for probabilistic modeling,
inference, and criticism. It is a testbed for fast experimentation and research
with probabilistic models, ranging from classical hierarchical models on small
data sets to complex deep probabilistic models on large data sets. Edward fuses
three fields: Bayesian statistics and machine learning, deep learning, and
probabilistic programming.
It supports __modeling__ with
+ Directed graphical models
+ Neural networks (via libraries such as
[`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers)
and
[Keras](http://keras.io))
+ Implicit generative models
+ Bayesian nonparametrics and probabilistic programs
It supports __inference__ with
+ Variational inference
+ Black box variational inference
+ Stochastic variational inference
+ Generative adversarial networks
+ Maximum a posteriori estimation
+ Monte Carlo
+ Gibbs sampling
+ Hamiltonian Monte Carlo
+ Stochastic gradient Langevin dynamics
+ Compositions of inference
+ Expectation-Maximization
+ Pseudo-marginal and ABC methods
+ Message passing algorithms
It supports __criticism__ of the model and inference with
+ Point-based evaluations
+ Posterior predictive checks
Edward is built on top of [TensorFlow](https://www.tensorflow.org).
It enables features such as computational graphs, distributed
training, CPU/GPU integration, automatic differentiation, and
visualization with TensorBoard.
## Resources
+ [Edward website](http://edwardlib.org)
+ [Edward Forum](http://discuss.edwardlib.org)
+ [Edward Gitter channel](http://gitter.im/blei-lab/edward)
+ [Edward releases](https://github.com/blei-lab/edward/releases)
+ [Edward papers, posters, and slides](https://github.com/edwardlib/papers)
See [Getting Started](http://edwardlib.org/getting-started) for how to install Edward.