Bayesim: a tool for adaptive grid model fitting with Bayesian inference
Data Analysis, Statistics and Probability
2019-11-28 v1 Materials Science
Applied Physics
Abstract
Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including semiconductor devices. In this work, we introduce Bayesim, a Python package that utilizes adaptive grid sampling to efficiently generate a probability distribution over multiple input parameters to a forward model using a collection of experimental measurements. We discuss the implementation choices made in the code, showcase two examples in photovoltaics, and discuss general prerequisites for the approach to apply to other systems.
Cite
@article{arxiv.1811.00421,
title = {Bayesim: a tool for adaptive grid model fitting with Bayesian inference},
author = {Rachel C. Kurchin and Giuseppe Romano and Tonio Buonassisi},
journal= {arXiv preprint arXiv:1811.00421},
year = {2019}
}