Y
Yuling Yao
Researcher at Columbia University
Publications - 30
Citations - 805
Yuling Yao is an academic researcher from Columbia University. The author has contributed to research in topics: Bayesian probability & Bayesian inference. The author has an hindex of 11, co-authored 25 publications receiving 466 citations.
Papers
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Journal ArticleDOI
Discussion of "Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al
TL;DR: In this article, the authors present a Bayesian analysis of Bayesian networks with a focus on the first-order dynamics of the Bayesian network, and include invited and contributed discussions.
Journal ArticleDOI
Using stacking to average Bayesian predictive distributions
TL;DR: This work takes the idea of stacking from the point estimation literature and generalizes to the combination of predictive distributions, extending the utility function to any proper scoring rule, using Pareto smoothed importance sampling to efficiently compute the required leave-one-out posterior distributions and regularization to get more stability.
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models [R package loo version 2.4.1]
Aki Vehtari,Jonah Gabry,Måns Magnusson,Yuling Yao,Paul-Christian Bürkner,Topi Paananen,Andrew Gelman +6 more
Posted Content
Bayesian Workflow.
Andrew Gelman,Aki Vehtari,Daniel Simpson,Charles C. Margossian,Bob Carpenter,Yuling Yao,Lauren Kennedy,Jonah Gabry,Paul-Christian Bürkner,Martin Modrak +9 more
TL;DR: The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory, and this work reviews all aspects of workflow in the context of several examples.
Proceedings Article
Yes, but did it work?: Evaluating variational inference
TL;DR: Two diagnostic algorithms are proposed that give a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate.