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2022

Bayesian Experimental Design for Symbolic Discovery

Clarkson, Kenneth L., Cornelio, Cristina, Dash, Sanjeeb et al.

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This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms.

We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior.
A step for computing the predictive distribution, involving convolution, is computed via either numerical integration, or via fast transform methods.

Bayesian Experimental Design for Symbolic Discovery · Around

Built on

A total entropy criterion for the dual problem of model discrimination and parameter estimation
David M Borth · 1975
Earlier work this paper cites.
Numerical methods for the design of large-scale nonlinear discrete ill-posed inverse problems
Eldad Haber, Lior Horesh, and Luis Tenorio · 2009
Earlier work this paper cites.
Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science
Daniel R. Cavagnaro, Jay I. Myung, Mark A. Pitt, and Janne V. Kujala · 2010
Earlier work this paper cites.

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Ai descartes: Combining data and theory for derivable scientific discovery
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