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# TensorFlow Probability

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference using automatic differentiation, and scalability to large datasets and models with hardware acceleration (GPUs) and distributed computation.

To get started with TensorFlow Probability, see the install guide and view the Python notebook tutorials.

## Components

Our probabilistic machine learning tools are structured as follows:

### Layer 0: TensorFlow

Numerical operations—in particular, the `LinearOperator` class—enables matrix-free implementations that can exploit a particular structure (diagonal, low-rank, etc.) for efficient computation. It is built and maintained by the TensorFlow Probability team and is part of `tf.linalg` in core TensorFlow.

### Layer 3: Probabilistic Inference

TensorFlow Probability is under active development and interfaces may change.

## Examples

In addition to the Python notebook tutorials listed in the navigation, there are some example scripts available:

## Report issues

Report bugs or feature requests using the TensorFlow Probability issue tracker.

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