Speaker
Samuel Wiqvist
(Lund University)
Description
Learning summary statistics is a fundamental problem in Approximate Bayesian Computation (ABC). The problem of learning summary statistics is in fact the main challenge when applying ABC in practice, and affects the resulting inference considerably. Deep learning methods have previously been used to learn summary statistics for ABC. Here we introduce a novel deep learning architecture (Partially Exchangeable Networks, PENs), with the purpose of automatically learning summary statistics for ABC. Our case studies show that our methodology outperforms other popular methods, resulting in more accurate ABC inference for Markovian data.
Joint work with Pierre-Alexandre Mattei, Umberto Picchini and Jes Frellsen.
Primary author
Samuel Wiqvist
(Lund University)
Co-authors
Pierre-Alexandre Mattei
(IT University of Copenhagen)
Umberto Picchini
(Departmentof Mathematical Sciences, Chalmers University of Technologyand the University of Gothenburg)
Jes Frellsen
(IT University of Copenhagen)
