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Found 5 papers, 3 papers with code
A Targeted Accuracy Diagnostic for Variational Approximations
Variational Inference (VI) is an attractive alternative to Markov Chain Monte Carlo (MCMC) due to its computational efficiency in the case of large datasets and/or complex models with high-dimensional parameters.
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
Kernel Stein discrepancy (KSD) is a widely used kernel-based measure of discrepancy between probability measures.
Validated Variational Inference via Practical Posterior Error Bounds
Finally, we demonstrate the utility of our proposed workflow and error bounds on a robust regression problem and on a real-data example with a widely used multilevel hierarchical model.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximation to the posterior distribution.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
We develop an approach to scalable approximate GP regression with finite-data guarantees on the accuracy of pointwise posterior mean and variance estimates.
