7 citations · 15 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
Bayesian Modular Inference for Copula Models with Potentially Misspecified Marginals
Lucas Kock, David T. Frazier, Michael Stanley Smith +1
Copula models of multivariate data are popular because they allow separate specification of marginal distributions and the copula function. These components can be treated as inter…
Predictive variational inference for flexible regression models
Lucas Kock, Scott A. Sisson, G. S. Rodrigues +1
A conventional Bayesian approach to prediction uses the posterior distribution to integrate out parameters in a density for unobserved data conditional on the observed data and par…