2019/02/07 by Takafumi Kajihara, Motonobu Kanagawa, Kajihara, Takafumi +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Evolution and Genetic Dynamics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1902.02517
32 pages
arxiv created 2019/02/07 · openalex publication_date 2019/02/07 · arxiv updated 2019/02/08 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
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.