A variational inference for the Lévy adaptive regression with multiple kernels

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...

Full description

Saved in:
Bibliographic Details
Published inComputational statistics Vol. 37; no. 5; pp. 2493 - 2515
Main Authors Lee, Youngseon, Jo, Seongil, Lee, Jaeyong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022
Springer Nature B.V
Subjects
Algorithms
Approximation
Computer simulation
Economic Theory/Quantitative Economics/Mathematical Methods
Inference
Kernels
Markov analysis
Markov chains
Mathematical analysis
Mathematics and Statistics
Methods
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Regression models
Simulated annealing
Simulation
Statistics
Lévy adaptive regression kernel model
Variational Bayes
Multiple kernels
Simulated annealing
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-022-01200-z