A review of ABC via density estimation, arguing that machine-learning conditional density estimators are the most promising route to scalable likelihood-free inference.
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
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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.
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2019 1verdicts
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A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models
A review of ABC via density estimation, arguing that machine-learning conditional density estimators are the most promising route to scalable likelihood-free inference.