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Bayesian inference is a widely used and powerful analytical technique infields such as astronomy and particle physics but has historically beenunderutilized in some other disciplines including semiconductor devices. Inthis work, we introduce Bayesim, a Python package that utilizes adaptive gridsampling to efficiently generate a probability distribution over multiple inputparameters to a forward model using a collection of experimental measurements.We discuss the implementation choices made in the code, showcase two examplesin photovoltaics, and discuss general prerequisites for the approach to applyto other systems.

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