• Corpus ID: 219956330

Distortion estimates for approximate Bayesian inference

@inproceedings{Xing2020DistortionEF,
  title={Distortion estimates for approximate Bayesian inference},
  author={Hanwen Xing and Geoff Nicholls and Jeong Eun Lee},
  booktitle={UAI},
  year={2020}
}
Current literature on posterior approximation for Bayesian inference offers many alternative methods. Does our chosen approximation scheme work well on the observed data? The best existing generic diagnostic tools treating this kind of question by looking at performance averaged over data space, or otherwise lack diagnostic detail. However, if the approximation is bad for most data, but good at the observed data, then we may discard a useful approximation. We give graphical diagnostics for… 
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References

SHOWING 1-10 OF 39 REFERENCES
Approximate inference for the loss-calibrated Bayesian
TLDR
This work proposes an EM-like algorithm on the Bayesian posterior risk and shows how it can improve a standard approach to Gaussian process classication when losses are asymmetric.
Calibrated Approximate Bayesian Inference
TLDR
It is shown that the original approximate inference had poor coverage for these data and should not be trusted, by exploiting the symmetry of the coverage error under permutation of low level group labels and smoothing with Bayesian Additive Regression Trees.
Calibration Procedures for Approximate Bayesian Credible Sets
TLDR
Two calibration procedures for checking the coverage of approximate Bayesian credible sets including intervals estimated using Monte Carlo methods are developed and applied.
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
TLDR
This work proposes a new approach to likelihood-free inference based on Bayesian conditional density estimation, which requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.
Diagnostic tools for approximate Bayesian computation using the coverage property
TLDR
Diagnostic tools for the choice of the kernel scale parameter based on assessing the coverage property are proposed, which asserts that credible intervals have the correct coverage levels in appropriately designed simulation settings.
Automatic Posterior Transformation for Likelihood-Free Inference
TLDR
Automatic posterior transformation (APT) is presented, a new sequential neural posterior estimation method for simulation-based inference that can modify the posterior estimate using arbitrary, dynamically updated proposals, and is compatible with powerful flow-based density estimators.
Bayesian inference with optimal maps
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Variational Bayesian Decision-making for Continuous Utilities
TLDR
This work presents an automatic pipeline that co-opts continuous utilities into variational inference algorithms to account for decision-making and provides practical strategies for approximating and maximizing the gain.
Proper likelihoods for Bayesian analysis
SUMMARY The validity of posterior probability statements follows from probability calculus when the likelihood is the density of the observations. To investigate other cases, a second, more intuitive…
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