gprob

Name	gprob JSON
Version	0.1.0 JSON
	download
home_page	None
Summary	Probabilistic programming with arrays of Gaussian variables.
upload_time	2024-09-15 17:56:23
maintainer	None
docs_url	None
author	None
requires_python	>=3.7
license	MIT License Copyright (c) 2024 Sergey A. Fedorov Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords	gaussian distribution noise random variables stochastic processes gaussian processes probabilistic programming python numpy
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            # gprob
gprob is a probabilistic programming language for Gaussian random variables with exact conditioning, implemented as a python package.

A brief example:
```python
from gprob import normal

# Initializing two independent normal variables.
x = normal(0, 1)
y = normal(0, 1)

# The joint distribution of x and y under the contition that 
# their sum is zero is obtained as 
z = (x & y) | {x-y: 0}

z.cov()
```



## Requirements
* python >= 3.7
* [numpy](https://numpy.org/)

## Installation

```
pip install gprob
```

## Acknowledgements
gprob was inspired by [GaussianInfer](https://github.com/damast93/GaussianInfer), an accompaniment for the paper

D. Stein and S. Staton, "Compositional Semantics for Probabilistic Programs with Exact Conditioning," 2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), Rome, Italy, 2021, pp. 1-13, doi: 10.1109/LICS52264.2021.9470552


            

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