9 citations · 13 across the 6 of their papers we have counts for
7 papers
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…
Predictive performance of power posteriors
Yann McLatchie, Edwin Fong, David T. Frazier +1
We analyse the impact of using tempered likelihoods in the production of posterior predictions. While the choice of temperature has an impact on predictive performance in small sam…
The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions
David Kohns, Noa Kallioinen, Yann McLatchie +1
We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced . Compared to other priors designed for times-ser…
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,…