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Found 8 papers, 3 papers with code
Bayes Hilbert Spaces for Posterior Approximation
This manuscript studies the application of Bayes Hilbert spaces to the posterior approximation problem.
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.
Grassmann Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo.
Variational Gaussian Processes: A Functional Analysis View
Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference.
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis
Statistical depth is the act of gauging how representative a point is compared to a reference probability measure.
A Kernel Two-Sample Test for Functional Data
We propose a nonparametric two-sample test procedure based on Maximum Mean Discrepancy (MMD) for testing the hypothesis that two samples of functions have the same underlying distribution, using kernels defined on function spaces.
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
We show that the maximum likelihood estimation of the scale parameter alone provides significant adaptation against misspecification of the Gaussian process model in the sense that the model can become "slowly" overconfident at worst, regardless of the difference between the smoothness of the data-generating function and that expected by the model.
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
In this setting, an important theoretical question of practial relevance is how accurate the Gaussian process approximations will be given the difficulty of the problem, our model and the extent of the misspecification.
