Pseudo-marginal Bayesian inference for Gaussian process latent variable models

DOI10.1007/S10994-021-05971-2zbMath1504.62032OpenAlexW3156383708MaRDI QIDQ2051307

Yanyan Li

Publication date: 24 November 2021

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-05971-2

zbMATH Keywords

Gaussian processapproximate inferencevariationalcollapsed Gibbs samplinglatent variable model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• GitHub

Cites Work

• Unnamed Item
• The pseudo-marginal approach for efficient Monte Carlo computations
• Accelerating pseudo-marginal MCMC using Gaussian processes
• An introduction to variational methods for graphical models
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
• An adaptive Metropolis algorithm

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