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04/18/2023
Bayes Hilbert Spaces for Posterior Approximation
Performing inference in Bayesian models requires sampling algorithms to ...
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06/09/2022
A Spectral 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 ...
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02/07/2022
Grassmann Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a deterministic particle in...
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10/25/2021
Variational Gaussian Processes: A Functional Analysis View
Variational Gaussian process (GP) approximations have become a standard ...
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05/26/2021
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 co...
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08/25/2020
A Kernel Two-Sample Test for Functional Data
We propose a nonparametric two-sample test procedure based on Maximum Me...
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01/29/2020
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Despite the ubiquity of the Gaussian process regression model, few theor...
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01/29/2020
