Uncertainty in Artificial Intelligence
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GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Edward Meeds, Max Welling
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.
Pages: 593-602
PS Link:
PDF Link: /papers/14/p593-meeds.pdf
AUTHOR = "Edward Meeds and Max Welling",
TITLE = "GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "593--602"

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