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A Bayes interpretation of stacking for M-complete and M-open settings

A Bayes interpretation of stacking for M-complete and M-open settings

16 February 2016
Tri Le
B. Clarke
ArXivPDFHTML

Papers citing "A Bayes interpretation of stacking for M-complete and M-open settings"

6 / 6 papers shown
Title
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
Daniel Waxman
Fernando Llorente
Petar M. Djurić
23
0
0
21 May 2025
Predictive variational inference: Learn the predictively optimal posterior distribution
Predictive variational inference: Learn the predictively optimal posterior distribution
Jinlin Lai
Yuling Yao
BDL
41
0
0
18 Oct 2024
MODL: Multilearner Online Deep Learning
MODL: Multilearner Online Deep Learning
Antonios Valkanas
Boris N. Oreshkin
Mark Coates
59
2
0
28 May 2024
BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging,
  Stacking and Hierarchical Stacking in Python
BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging, Stacking and Hierarchical Stacking in Python
Nathaniel Haines
Conor Goold
MoMe
30
1
0
30 Apr 2024
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of
  Multimodal Posteriors
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao
Aki Vehtari
Andrew Gelman
39
60
0
22 Jun 2020
Using stacking to average Bayesian predictive distributions
Using stacking to average Bayesian predictive distributions
Yuling Yao
Aki Vehtari
Daniel P. Simpson
Andrew Gelman
43
336
0
06 Apr 2017
1