Spreadsheet Probabilistic Programming
This provides spreadsheet end-users with accessible probabilistic modeling for decision-making under uncertainty, representing a novel application domain rather than an incremental advance in core ML methods.
The paper tackles the problem of performing Bayesian inference on spreadsheet computations by enabling probabilistic programming directly within spreadsheet applications like Excel, achieving probabilistically coherent inference even with user-defined black-box functions.
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.