Variational inference for cutting feedback in misspecified models
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Publication:6181748
DOI10.1214/23-sts886arXiv2108.11066MaRDI QIDQ6181748
Michael Stanley Smith, Xuejun Yu, David J. Nott
Publication date: 23 January 2024
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.11066
Cites Work
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- Feedback and modularization in a Bayesian meta-analysis of tree traits affecting forest dynamics
- Fixed-form variational posterior approximation through stochastic linear regression
- Stochastic approximation cut algorithm for inference in modularized Bayesian models
- Interoperability of statistical models in pandemic preparedness: principles and reality
- Fast and accurate variational inference for models with many latent variables
- Monte Carlo co-ordinate ascent variational inference
- Checking for prior-data conflict using prior-to-posterior divergences
- Bayesian Calibration of Computer Models
- On the properties of variational approximations of Gibbs posteriors
- Errors-in-Variables in Joint Population Pharmacokinetic/Pharmacodynamic Modeling
- Explaining Variational Approximations
- Sequential Monte Carlo Samplers
- Adjustment for Missing Confounders Using External Validation Data and Propensity Scores
- Model Feedback in Bayesian Propensity Score Estimation
- Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance
- High-Dimensional Copula Variational Approximation Through Transformation
- Unbiased Markov Chain Monte Carlo Methods with Couplings
- A General Framework for Updating Belief Distributions
- Modularization in Bayesian analysis, with emphasis on analysis of computer models
- Cuts in Bayesian graphical models
- Conflict diagnostics in directed acyclic graphs, with applications in Bayesian evidence synthesis
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