Bayesim: a tool for adaptive grid model fitting with Bayesian inference

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.

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• The impact of priors and observables on parameter inferences in the Constrained MSSM
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Cited by in corpus (3)

• How much physics is in a current-voltage curve? Inferring defect properties from photovoltaic device measurements
• Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization
• Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics