# PyProcess

**Repository Path**: mirrors_lepy/PyProcess

## Basic Information

- **Project Name**: PyProcess
- **Description**: Generate stochastic processes using Python. Unfortunately not maintained any longer =(
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-08-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

PyProcess 0.2
=============

PyProcess is a Python class library used to exactly simulate stochastic processes, and their properties.

Using this library, you can simulate the following random processes:

Continuous Diffusions
- Brownian Motion
- Geometric Brownian Motion
- CEV
- CIR
- Square Bessel Process 
- Ornstein Uhlenbeck process
- Time-integrated Ornstein Uhlenbeck process
- Levy Processes
- Bessel Process (coming soon)
- Fractional Brownian Motion (coming soon)

Jump Diffusions
- Gamma process
- Variance-gamma process
- Geometric Gamma process
- Inverse Gaussian process *NEW*
- Normal Inverse Gaussian process *NEW*

Step Processes
- Renewal process
- Poisson process
- Compound poisson process
- marked-poisson process
- Fractional poisson process (coming soon)


*See fun examples of the processes you can simulate [here] (http://pyprocess.70percentfatfree.com)*


Simulation Algorithms + PyProcess 0.2
-------------------------------------

See the report for [PyProcess 0.2's background.](http://www.camdp.com/mediaFiles/PDFs/PyProcess.pdf)



Lastly
-----------------
Visit me at [camdp.com](http://www.camdp.com) or  at [@cmrn_dp](http://twitter.com/cmrn_dp)