GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Edward Meeds, Max Welling
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:598-607, 2014.

Abstract

Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-meeds14a, title = {{GPS}-{ABC}: {G}aussian Process Surrogate Approximate {B}ayesian Computation}, author = {Meeds, Edward and Welling, Max}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {598--607}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/meeds14a/meeds14a.pdf}, url = {https://proceedings.mlr.press/r12/meeds14a.html}, abstract = {Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation %A Edward Meeds %A Max Welling %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-meeds14a %I PMLR %P 598--607 %U https://proceedings.mlr.press/r12/meeds14a.html %V R12 %X Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms. %Z Reissued by PMLR on 04 October 2026.
APA
Meeds, E. & Welling, M.. (2014). GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:598-607 Available from https://proceedings.mlr.press/r12/meeds14a.html. Reissued by PMLR on 04 October 2026.

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