7 citations · 15 across the 11 of their papers we have counts for
12 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…
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…
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…
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…
Deep mixture of linear mixed models for complex longitudinal data
Lucas Kock, Nadja Klein, David J. Nott
Mixtures of linear mixed models are widely used for modelling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are de…
Truly Multivariate Structured Additive Distributional Regression
Lucas Kock, Nadja Klein
Generalized additive models for location, scale and shape (GAMLSS) are a popular extension to mean regression models where each parameter of an arbitrary distribution is modelled t…
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Benedikt Lütke Schwienhorst, Lucas Kock, Nadja Klein +1
Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalize…
Pooling information in likelihood-free inference
David T. Frazier, Christopher Drovandi, Lucas Kock +1
Likelihood-free inference (LFI) methods, such as approximate Bayesian computation, have become commonplace for conducting inference in complex models. Many approaches are based on…
Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
Lucas Kock, Nadja Klein, David J. Nott
Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure…