8 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…
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…
BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme +2
Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable fea…
Variational inference for hierarchical models with conditional scale and skewness corrections
Lucas Kock, Linda S. L. Tan, Prateek Bansal +1
Gaussian variational approximations are widely used for summarizing posterior distributions in Bayesian models, especially in high-dimensional settings. However, a drawback of such…
Scalable Variational Inference for Multinomial Probit Models under Large Choice Sets and Sample Sizes
Gyeongjun Kim, Yeseul Kang, Lucas Kock +2
The multinomial probit (MNP) model is widely used to analyze categorical outcomes due to its ability to capture flexible substitution patterns among alternatives. Conventional like…