1 citations · 2 across the 6 of their papers we have counts for
6 papers
Logistic Gaussian process density regression: a generalized Bayesian approach
Zichuan Chen, Lucas Kock, Jeong Eun Lee +1
Density regression extends conventional parametric regression by allowing the entire distribution of the response to vary flexibly with covariates rather than just low-order moment…
Learning the distance for ABC and localized neural posterior estimation
Yuyan Wang, David J. Nott
Likelihood-free inference methods can perform Bayesian inference when evaluating the likelihood is impractical but simulating synthetic data from the model is feasible. Approximate…
Calibrated Multivariate Regression with Localized PIT Mappings
Lucas Kock, G. S. Rodrigues, Scott A. Sisson +2
Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic for…
Positional Encoder Graph Quantile Neural Networks for Geographic Data
William E. R. de Amorim, Scott A. Sisson, T. Rodrigues +2
Positional Encoder Graph Neural Networks (PE-GNNs) are among the most effective models for learning from continuous spatial data. However, their predictive distributions are often…
Model-Free Local Recalibration of Neural Networks
R. Torres, D. J. Nott, S. A. Sisson +3
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been m…
Regression Copulas for Multivariate Responses
Nadja Klein, Michael Stanley Smith, David Nott +1
We propose a novel distributional regression model for a multivariate response vector based on a copula process over the covariate space. It uses the implicit copula of a Gaussian…