Pseudo-marginal Bayesian inference for Gaussian process latent variable models
type: Item
instance of: scholarly article
title: Pseudo-marginal Bayesian inference for Gaussian process latent variable models
zbMATH Open document ID: 1504.62032
author: Yanyan Li
published in: Machine Learning
publication date: 2021-11-24T00:00:00Z
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7432831
zbMATH Keywords: Gaussian process
zbMATH Keywords: latent variable model
zbMATH Keywords: approximate inference
zbMATH Keywords: variational
zbMATH Keywords: collapsed Gibbs sampling
MaRDI profile type: Publication
full work available at URL: s10994-021-05971-2
OpenAlex ID: W3156383708
cites work: The pseudo-marginal approach for efficient Monte Carlo computations
cites work: Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
cites work: Accelerating pseudo-marginal MCMC using Gaussian processes
cites work: An adaptive Metropolis algorithm
cites work: An introduction to variational methods for graphical models
cites work: Q3093318
DOI: 10.1007/S10994-021-05971-2
describes a project that uses: GitHub
description: scientific article; zbMATH DE number 7432831