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arXiv · 1902.02517

Model Selection for Simulator-based Statistical Models: A Kernel Approach

Takafumi Kajihara·Motonobu Kanagawa·Yuuki Nakaguchi·Kanishka Khandelwal·Kenji Fukumiziu
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Abstract

We propose a novel approach to model selection for simulator-based statistical models. The proposed approach defines a mixture of candidate models, and then iteratively updates the weight coefficients for those models as well as the parameters in each model simultaneously; this is done by recursively applying Bayes' rule, using the recently proposed kernel recursive ABC algorithm. The practical advantage of the method is that it can be used even when a modeler lacks appropriate prior knowledge about the parameters in each model. We demonstrate the effectiveness of the proposed approach with a number of experiments, including model selection for dynamical systems in ecology and epidemiology.

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Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumiziu. 2019-02-07. Model Selection for Simulator-based Statistical Models: A Kernel Approach. https://arxiv.org/abs/1902.02517

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