Uncertainty quantification for the horseshoe (with discussion)
Uncertainty Quantification for the Horseshoe (with Discussion) Page 1 Bayesian Analysis (2017)
12, Number 4, pp. 1221–1274 Uncertainty Quantification for the Horseshoe (with Discussion) …
12, Number 4, pp. 1221–1274 Uncertainty Quantification for the Horseshoe (with Discussion) …
Variational Gaussian processes for linear inverse problems
By now Bayesian methods are routinely used in practice for solving inverse problems. In
inverse problems the parameter or signal of interest is observed only indirectly, as an image …
inverse problems the parameter or signal of interest is observed only indirectly, as an image …
[PDF][PDF] Bayesian nonparametric statistics, St-Flour lecture notes
IÃĢ Castillo - arxiv preprint arxiv:2402.16422, 2024 - arxiv.org
arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 1 Bay!ian nonparamet"c #at$tics St-Flour
lecture notes Ismaël Castillo arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 2 2 Principe. Si …
lecture notes Ismaël Castillo arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 2 2 Principe. Si …
A review of uncertainty quantification for density estimation
S McDonald, D Campbell - 2021 - projecteuclid.org
A review of uncertainty quantification for density estimation Page 1 Statistics Surveys Vol. 15
(2021) 1–71 ISSN: 1935-7516 https://doi.org/10.1214/21-SS130 A review of uncertainty …
(2021) 1–71 ISSN: 1935-7516 https://doi.org/10.1214/21-SS130 A review of uncertainty …
Needles and straw in a haystack: robust confidence for possibly sparse sequences
E Belitser, N Nurushev - 2020 - projecteuclid.org
Needles and straw in a haystack: Robust confidence for possibly sparse sequences Page 1
Bernoulli 26(1), 2020, 191–225 https://doi.org/10.3150/19-BEJ1122 Needles and straw in a …
Bernoulli 26(1), 2020, 191–225 https://doi.org/10.3150/19-BEJ1122 Needles and straw in a …
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression
We investigate the frequentist guarantees of the variational sparse Gaussian process
regression model. In the theoretical analysis, we focus on the variational approach with …
regression model. In the theoretical analysis, we focus on the variational approach with …
Spike and slab empirical Bayes sparse credible sets
I Castillo, B Szabó - 2020 - projecteuclid.org
Spike and slab empirical Bayes sparse credible sets Page 1 Bernoulli 26(1), 2020, 127–158
https://doi.org/10.3150/19-BEJ1119 Spike and slab empirical Bayes sparse credible sets …
https://doi.org/10.3150/19-BEJ1119 Spike and slab empirical Bayes sparse credible sets …
Can we trust Bayesian uncertainty quantification from Gaussian process priors with squared exponential covariance kernel?
A Hadji, B Szabó - SIAM/ASA Journal on Uncertainty Quantification, 2021 - SIAM
We investigate the frequentist coverage properties of credible sets resulting from Gaussian
process priors with squared exponential covariance kernel. First, we show that by selecting …
process priors with squared exponential covariance kernel. First, we show that by selecting …
Statistical guarantees for stochastic Metropolis-Hastings
S Bieringer, G Kasieczka, MF Steffen… - arxiv preprint arxiv …, 2023 - arxiv.org
A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo
methods in uncertainty quantification. By calculating acceptance probabilities on batches, a …
methods in uncertainty quantification. By calculating acceptance probabilities on batches, a …
Bayesian dyadic trees and histograms for regression
Many machine learning tools for regression are based on recursive partitioning of the
covariate space into smaller regions, where the regression function can be estimated locally …
covariate space into smaller regions, where the regression function can be estimated locally …