Name | EMLM JSON |
Version |
0.1
JSON |
| download |
home_page | https://github.com/yourusername/EMLM |
Summary | A package for Bayesian linear regression with mixture models |
upload_time | 2024-02-17 12:23:09 |
maintainer | |
docs_url | None |
author | Your Name |
requires_python | |
license | MIT |
keywords |
|
VCS |
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bugtrack_url |
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requirements |
No requirements were recorded.
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Travis-CI |
No Travis.
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#EMLM
EMLM is a Python package for Bayesian linear regression with mixture models.
Overview
EMLM implements a Bayesian approach to linear regression with mixture models, allowing for more flexible and robust modeling of complex data structures. It combines the power of Bayesian inference with the flexibility of mixture models to handle heteroscedasticity, outliers, and other challenging data patterns.
Features
Bayesian linear regression with mixture models
Flexible handling of heteroscedasticity and outliers
Markov Chain Monte Carlo (MCMC) sampling for parameter estimation
Parallelization support for faster computation
Easy-to-use interface for model fitting and inference
Installation
You can install EMLM via pip:
```
pip install EMLM
```
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