GaussED: A Probabilistic Programming Language for Sequential Experimental Design

TL;DR

GaussED is a probabilistic programming language that automates sequential experimental design for Gaussian process models, enabling efficient solutions to diverse problems like PDEs, tomography, and Bayesian optimization.

Contribution

Introduction of GaussED, a versatile probabilistic programming language that simplifies and automates sequential experimental design tasks across various applications.

Findings

Automates experimental design for Gaussian processes

Supports solving PDEs, tomography, and Bayesian optimization

Reduces need for bespoke software in experimental tasks

Abstract

Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke software has been developed, but the approach is general and many of the actual computations performed in such software are identical. Motivated by the diverse problems that can in principle be solved with common code, this paper presents GaussED, a simple probabilistic programming language coupled to a powerful experimental design engine, which together automate sequential experimental design for approximating a (possibly nonlinear) quantity of interest in Gaussian processes models. Using a handful of commands, GaussED can be used to: solve linear partial differential equations, perform tomographic reconstruction from integral data and implement Bayesian optimisation with gradient data.