Edward

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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 and Keras)
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 TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

Tags: library python probabalistic

Last modified 16 December 2024

Edward

A library for probabilistic modeling, inference, and criticism.