https://github.com/daniel-furman/lwmcmc

MCMC parameter space sampling via Metropolis-Hastings
https://github.com/daniel-furman/lwmcmc

bayesian-inference mcmc metropolis-hastings-algorithm numpy parameter-space-sampling

Last synced: 4 days ago
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MCMC parameter space sampling via Metropolis-Hastings

• Host: GitHub
• URL: https://github.com/daniel-furman/lwmcmc
• Owner: daniel-furman
• License: mit
• Created: 2021-02-03T19:53:48.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-08-15T19:02:40.000Z (over 3 years ago)
• Last Synced: 2024-10-12T11:08:25.171Z (about 1 month ago)
• Topics: bayesian-inference, mcmc, metropolis-hastings-algorithm, numpy, parameter-space-sampling
• Language: Python
• Homepage: https://libraries.io/pypi/lwMCMC
• Size: 7.43 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

[![Build Status](https://travis-ci.com/daniel-furman/lwMCMC.svg?branch=main)](https://travis-ci.com/daniel-furman/lwMCMC)

## lwMCMC: lightweight Markov Chain Monte Carlo

---

`from lwMCMC import MCMC`

Parameter space sampling with MCMC. See Bayesian inference with the MCMC class below, for an Experimental Geophysics regression. 

Posterior distributions | MCMC model fit

:---------------------------------:|:----------------------------------------:

![](examples/data/grid_ice.png) | ![](examples/data/ice_scatter.png)

* The grid entries reveal the 1-dimensional posterior distributions of our parameters after setting our prior beliefs, as well as the pairwise projections with one and two sigma error contours. 

* With the slope parameters's 1.8 +- 0.225 prior, the Bayesian inferred slope is 1.70 +- 0.17.

---

### Package Layout

* [LICENSE](https://github.com/daniel-furman/lwMCMC/blob/main/LICENSE) - the MIT license, which applies to this package

* README.md - the README file, which you are now reading

* [requirements.txt](https://github.com/daniel-furman/lwMCMC/blob/main/requirements.txt) - prerequisites to install this package, used by pip

* [setup.py](https://github.com/daniel-furman/lwMCMC/blob/main/setup.py) - installer script

* [docs](https://github.com/daniel-furman/lwMCMC/tree/main/docs)/ - contains documentation on package installation and usage

* [examples](https://github.com/daniel-furman/lwMCMC/tree/main/examples)/ - use cases for Bayesian Modeling

* [lwMCMC](https://github.com/daniel-furman/lwMCMC/tree/main/lwMCMC)/ - the library code itself

* [tests](https://github.com/daniel-furman/lwMCMC/tree/main/test)/ - unit tests