A variational inference for the Lévy adaptive regression with multiple kernels

DOI10.1007/s00180-022-01200-zzbMath1505.62242OpenAlexW4210827334MaRDI QIDQ2095764

Jaeyong Lee, Young-Seon Lee, Seongil Jo

Publication date: 15 November 2022

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-022-01200-z

zbMATH Keywords

simulated annealingvariational Bayesmultiple kernelsLévy adaptive regression kernel model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Density estimation (62G07)

Uses Software

• PMTK
• ADADELTA

Cites Work

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• Optimization by Simulated Annealing
• Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Variational inference for count response semiparametric regression
• Variational inference for sparse spectrum Gaussian process regression
• Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm
• Jump-preserving regression and smoothing using local linear fitting: a compromise
• Change-points in nonparametric regression analysis
• A variational Bayes approach to a semiparametric regression using Gaussian process priors
• Gaussian variational approximation with sparse precision matrices
• Bayesian methods for neural networks and related models
• An introduction to variational methods for graphical models
• Kernel estimation of discontinuous regression functions
• Bayesian curve fitting for discontinuous functions using an overcomplete system with multiple kernels
• Conditionally structured variational Gaussian approximation with importance weights
• The discrete infinite logistic normal distribution
• Knot selection for least-squares and penalized splines
• Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data
• Explaining Variational Approximations
• Search and Optimization by Metaheuristics
• Gaussian Variational Approximation With a Factor Covariance Structure
• Graphical Models, Exponential Families, and Variational Inference
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Ideal spatial adaptation by wavelet shrinkage
• A jump-preserving curve fitting procedure based on local piecewise-linear kernel estimation
• On Information and Sufficiency
• Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression