Logistic Gaussian process density regression: a generalized Bayesian approach (opens in new tab)

Density regression extends conventional parametric regression by allowing the entire distribution of the response to vary flexibly with covariates rather than just low-order moments. In the Bayesian setting, logistic Gaussian process (GP) priors have been widely used for density estimation and extend naturally to density regression. The prior can be centred on a base density model, with the nonparametric component providing an interpretable corr...