  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.
