  Spreadsheet workbook contents are simple programs. Because of this,
probabilistic programming techniques can be used to perform Bayesian inversion
of spreadsheet computations. What is more, existing execution engines in
spreadsheet applications such as Microsoft Excel can be made to do this using
only built-in functionality. We demonstrate this by developing a native Excel
implementation of both a particle Markov Chain Monte Carlo variant and
black-box variational inference for spreadsheet probabilistic programming. The
resulting engine performs probabilistically coherent inference over spreadsheet
computations, notably including spreadsheets that include user-defined
black-box functions. Spreadsheet engines that choose to integrate the
functionality we describe in this paper will give their users the ability to
both easily develop probabilistic models and maintain them over time by
including actuals via a simple user-interface mechanism. For spreadsheet
end-users this would mean having access to efficient and probabilistically
coherent probabilistic modeling and inference for use in all kinds of decision
making under uncertainty.
