[HTML][HTML] Semi-supervised regression using diffusion on graphs
In real-world machine learning applications, unlabeled training data are readily available,
but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning …
but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning …
Scalable inference for crossed random effects models
We develop methodology and complexity theory for Markov chain Monte Carlo algorithms
used in inference for crossed random effects models in modern analysis of variance. We …
used in inference for crossed random effects models in modern analysis of variance. We …
Conjugate gradient methods for high-dimensional GLMMs
Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The
main bottleneck of many computational approaches lies in the inversion of the high …
main bottleneck of many computational approaches lies in the inversion of the high …
Scalable Bayesian computation for crossed and nested hierarchical models
O Papaspiliopoulos, T Stumpf-Fétizon… - arXiv preprint arXiv …, 2021 - arxiv.org
We develop sampling algorithms to fit Bayesian hierarchical models, the computational
complexity of which scales linearly with the number of observations and the number of …
complexity of which scales linearly with the number of observations and the number of …
Scalable Bayesian computation for crossed and nested hierarchical models
O Papaspiliopoulos, T Stumpf-Fétizon… - Electronic Journal of …, 2023 - projecteuclid.org
We develop sampling algorithms to fit Bayesian hierarchical models, the computational
complexity of which scales linearly with the number of observations and the number of …
complexity of which scales linearly with the number of observations and the number of …
Multilevel linear models, Gibbs samplers and multigrid decompositions (with discussion)
Multilevel Linear Models, Gibbs Samplers and Multigrid Decompositions (with Discussion)
Page 1 Bayesian Analysis (2021) 16, Number 4, pp. 1309–1391 Multilevel Linear Models …
Page 1 Bayesian Analysis (2021) 16, Number 4, pp. 1309–1391 Multilevel Linear Models …