https://github.com/wilhelmbuitrago/probabilistic-functions
A comprehensive Python library for working with probability distributions and statistical functions. This library provides tools for symbolic and numeric manipulation of probability distributions, along with visualization capabilities.
https://github.com/wilhelmbuitrago/probabilistic-functions
distributions learning symbolic-math
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A comprehensive Python library for working with probability distributions and statistical functions. This library provides tools for symbolic and numeric manipulation of probability distributions, along with visualization capabilities.
- Host: GitHub
- URL: https://github.com/wilhelmbuitrago/probabilistic-functions
- Owner: WilhelmBuitrago
- License: apache-2.0
- Created: 2025-05-22T03:23:47.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-08-05T21:17:52.000Z (2 months ago)
- Last Synced: 2025-09-07T05:44:35.975Z (29 days ago)
- Topics: distributions, learning, symbolic-math
- Language: Jupyter Notebook
- Homepage:
- Size: 1.65 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Probabilistic Functions
A comprehensive Python library for working with probability distributions and statistical functions. This library provides tools for symbolic and numeric manipulation of probability distributions, along with visualization capabilities.
## ⚠️ Requirements
> **Warning**
This library is designed to work primarily in Jupyter environments with LaTeX support. Some functionality may not work correctly outside of this environment.
- Python 3.13+
- Jupyter Notebook/Lab
- LaTeX installation for proper equation rendering
## Installation
Install the library using pip:
```bash
pip install probabilistic-functions
```
## Features
- Symbolic representation of probability distributions
- Calculation of probability mass/density functions (PMF/PDF)
- Calculation of cumulative distribution functions (CDF)
- Statistical properties (mean, variance, etc.)
- Visualization of distributions with customizable parameters
- Support for both discrete and continuous distributions
## Supported Distributions
### Discrete Distributions
- Bernoulli
- Binomial
- Geometric
- Hypergeometric
- Poisson
### Continuous Distributions
- Normal (Gaussian)
- Exponential
- Uniform
- Weibull
- Gamma
- Beta
- LogNormal
- Lindley
## Experimental Distributions (Limited Support)
The following distributions are included in the library, but their functionality may be limited or unstable:
- Burr
- Pareto
- Cauchy
- Laplace
- Gumbel
> **Note:**
Support for these distributions is under development. Some functions may not be fully implemented or may produce unexpected results.
## Usage Examples
```python
from probabilistic_functions.core import Binomial, Normal
from probabilistic_functions.plots import plot_function
# Plot a binomial distribution
binomial = Binomial()
plot_function(binomial, "pmf", {"n": 10, "p": 0.5})
# Plot multiple normal distributions
normal = Normal()
plot_function(normal, "pdf", {"m": [0, 1], "v": [1, 2]})
```
## Working with Multiple Parameters
You can plot multiple parameter combinations by passing lists:
```python
# Plot multiple Poisson distributions with different lambda values
from probabilistic_functions.core import Poisson
poisson = Poisson()
plot_function(poisson, "pmf", {"l": [1, 5, 10]})
```
## Changelog
For a detailed list of changes between versions, please see the [Changelog](https://github.com/WilhelmBuitrago/probabilistic-functions/blob/main/CHANGELOG.md).
## License
[Apache License 2.0](https://github.com/WilhelmBuitrago/probabilistic-functions/blob/main/LICENSE)
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.