Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance

DOI10.1080/10618600.2021.1880921OpenAlexW3129095218WikidataQ115005224 ScholiaQ115005224MaRDI QIDQ5066483

Nadja Klein, David J. Nott, Minh-Ngoc Tran, Xuejun Yu

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.08725

zbMATH Keywords

Gaussian processMarkov chain Monte Carlovariational approximationlikelihood-free inferencedeep neural networklaw of total variance

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

An improved approximate Bayesian computation scheme for parameter inference based on a recalibration post-processing method ⋮ Implicit Copula Variational Inference ⋮ Variational inference for cutting feedback in misspecified models ⋮ Fast Variational Bayes Methods for Multinomial Probit Models

Uses Software

• BayesDA
• RStan
• GPfit
• rstan
• abcrf
• VBayesLab

Cites Work

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• Explaining Variational Approximations
• HIV with contact tracing: a case study in approximate Bayesian computation
• Bayesian Deep Net GLM and GLMM
• Model Misspecification in Approximate Bayesian Computation: Consequences and Diagnostics
• Approximate Bayesian Computation: A Nonparametric Perspective
• Getting It Right

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