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 demand- ing simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely chal- lenging. The Approximate Bayesian Computa- tion (ABC) framework is the standard statisti- cal tool to handle these likelihood free problems, but they require a very large number of simula- tions. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior in- ference. Both algorithms use confidence esti- mates for the accept probability in the Metropo- lis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algo- rithm 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 biologi- cal problem illustrate the potential of these algo- rithms.

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 demand- ing simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely chal- lenging. The Approximate Bayesian Computa- tion (ABC) framework is the standard statisti- cal tool to handle these likelihood free problems, but they require a very large number of simula- tions. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior in- ference. Both algorithms use confidence esti- mates for the accept probability in the Metropo- lis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algo- rithm 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 biologi- cal problem illustrate the potential of these algo- rithms.}, 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 demand- ing simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely chal- lenging. The Approximate Bayesian Computa- tion (ABC) framework is the standard statisti- cal tool to handle these likelihood free problems, but they require a very large number of simula- tions. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior in- ference. Both algorithms use confidence esti- mates for the accept probability in the Metropo- lis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algo- rithm 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 biologi- cal problem illustrate the potential of these algo- rithms. %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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