A variational inference for the Lévy adaptive regression with multiple kernels

Youngseon Lee, Seongil Jo and Jaeyong Lee
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Youngseon Lee: Samsung SDS
Seongil Jo: Inha University
Jaeyong Lee: Seoul National University

Computational Statistics, 2022, vol. 37, issue 5, No 16, 2493-2515

Abstract: Abstract This paper presents a variational Bayes approach to a Lévy adaptive regression kernel (LARK) model that represents functions with an overcomplete system. In particular, we develop a variational inference method for a LARK model with multiple kernels (LARMuK) which estimates arbitrary functions that could have jump discontinuities. The algorithm is based on a variational Bayes approximation method with simulated annealing. We compare the proposed algorithm to a simulation-based reversible jump Markov chain Monte Carlo (RJMCMC) method using numerical experiments and discuss its potential and limitations.

Keywords: Lévy adaptive regression kernel model; Multiple kernels; Simulated annealing; Variational Bayes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-022-01200-z

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