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 |
|---|---|
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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.
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With the slope parameters's 1.8 +- 0.225 prior, the Bayesian inferred slope is 1.70 +- 0.17.
Package Layout
- LICENSE - the MIT license, which applies to this package
- README.md - the README file, which you are now reading
- requirements.txt - prerequisites to install this package, used by pip
- setup.py - installer script
- docs/ - contains documentation on package installation and usage
- examples/ - use cases for Bayesian Modeling
- lwMCMC/ - the library code itself
- tests/ - unit tests
