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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: 19 days ago
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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 (over 8 years ago)
Default Branch: master
Last Pushed: 2024-03-18T16:23:03.000Z (3 months ago)
Last Synced: 2024-03-19T15:31:57.315Z (3 months 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: 4,811
Watchers: 275
Forks: 766
Open Issues: 221
Metadata Files:
- Readme: README.md
- License: LICENSE.txt

Lists

awesome-stars - blei-lab/edward - A probabilistic programming language in TensorFlow. Deep generative models, variational inference. (Jupyter Notebook)
awesome-stars - blei-lab/edward - A probabilistic programming language in TensorFlow. Deep generative models, variational inference. (Jupyter Notebook)
embedded-ai.bi-weekly - blei-lab/edward: A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow
awesome-stars-copy - blei-lab/edward - A probabilistic programming language in TensorFlow. Deep generative models, variational inference. (Jupyter Notebook)
awesome-python-data-science - Edward - Probabilistic programming language in TensorFlow. (Deep Learning Tools)
awesome-Interpretable-ML - Edward
alex-mikhalev-awesome-stars - edward - A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow. (Python)
awesome-python-machine-learning-resources - GitHub - 36% open · ⏱️ 25.07.2018): (概率统计)
awesome-python-machine-learning - Edward - Edward is a Python library for probabilistic modeling, inference, and criticism. (Uncategorized / Uncategorized)
awesome-pysci - edward - バックエンドにTensorFlowやStanなどを選べるイケてる変分ベイズ推定ライブラリ (ベイズ推定，確率論的プログラミング)
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)
my-awesome-stars - blei-lab/edward - A probabilistic programming language in TensorFlow. Deep generative models, variational inference. (Jupyter Notebook)

README

        [![edward](../master/docs/images/edward_200.png?raw=true)](http://edwardlib.org)

[![Build Status](https://travis-ci.org/blei-lab/edward.svg?branch=master)](https://travis-ci.org/blei-lab/edward)

[![Coverage Status](https://coveralls.io/repos/github/blei-lab/edward/badge.svg?branch=master&cacheBuster=1)](https://coveralls.io/github/blei-lab/edward?branch=master)

[![Join the chat at https://gitter.im/blei-lab/edward](https://badges.gitter.im/blei-lab/edward.svg)](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.