9 citations · 13 across the 6 of their papers we have counts for
5 papers · 1 filter
Markov chain Monte Carlo for predictively oriented posteriors
Yann McLatchie, Louis Sharrock, David T. Frazier +1
The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore ca…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Efficient estimation and correction of selection-induced bias with order statistics
Yann McLatchie, Aki Vehtari
Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates. However, the decision-making…
Advances in projection predictive inference
Yann McLatchie, Sölvi Rögnvaldsson, Frank Weber +1
The concepts of Bayesian prediction, model comparison, and model selection have developed significantly over the last decade. As a result, the Bayesian community has witnessed a ra…
Bayesian order identification of ARMA models with projection predictive inference
Yann McLatchie, Asael Alonzo Matamoros, David Kohns +1
Auto-regressive moving-average (ARMA) models are ubiquitous forecasting tools. Parsimony in such models is highly valued for their interpretability and computational tractability,…