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Found 11 papers, 8 papers with code
Simulation-based stacking
Simulation-based inference has been popular for amortized Bayesian computation.
Variational Inference with Gaussian Score Matching
We study how GSM-VI behaves as a function of the problem dimensionality, the condition number of the target covariance matrix (when the target is Gaussian), and the degree of mismatch between the approximating and exact posterior distribution.
Discriminative calibration: Check Bayesian computation from simulations and flexible classifier
To check the accuracy of Bayesian computations, it is common to use rank-based simulation-based calibration (SBC).
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition
Currently, these predictive distributions are almost exclusively combined using linear mixtures such as Bayesian model averaging, Bayesian stacking, and mixture of experts.
Bayesian hierarchical stacking: Some models are (somewhere) useful
We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model.
Adaptive Path Sampling in Metastable Posterior Distributions
The normalizing constant plays an important role in Bayesian computation, and there is a large literature on methods for computing or approximating normalizing constants that cannot be evaluated in closed form.
Computation Methodology
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty.
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
Two main obstacles preventing the widespread adoption of variational Bayesian neural networks are the high parameter overhead that makes them infeasible on large networks, and the difficulty of implementation, which can be thought of as "programming overhead."
Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation.
Using stacking to average Bayesian predictive distributions
The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit.
Methodology Computation
Pareto Smoothed Importance Sampling
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail.
