7 citations · 7 across the 3 of their papers we have counts for
8 papers
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Satya Borgohain, Klaus Ackermann, Ruben Loaiza-Maya
Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings…
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Scalable Bayesian estimation in the multinomial probit model
Ruben Loaiza-Maya, Didier Nibbering
The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ…
Fast and Accurate Variational Inference for Models with Many Latent Variables
Rubén Loaiza-Maya, Michael Stanley Smith, David J. Nott +1
Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard app…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…