Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
Abstract
Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location. Logistic Gaussian processes (LGP) give a flexible prior for such densities. However, the normalizing constant of an LGP has no closed form, so existing methods approximate or replace the likelihood. We propose the exact likelihood finite-rank LGP (ExFR-LGP), which writes the log density as the sum of two bivariate functions, one of the response and a location-varying linear index of the covariates, and one of the response and the location. Each function is assigned a finite-rank Gaussian process prior that is piecewise linear on a regular grid, enabling the normalizing constant, the conditional mean and the quantiles to enjoy closed form representations. Posterior samples are drawn from the exact posterior under this prior via a Gibbs sampler that updates the Gaussian components by elliptical slice sampling. When the true log density is the sum of two such bivariate functions, we show that the posterior contracts at the minimax rate of an α-smooth bivariate density up to a logarithmic factor. Numerical experiments using synthetic data demonstrate the effectiveness of ExFR-LGP in conditional density estimation. Furthermore, application to fractional anisotropy responses along the corpus callosum in the Alzheimer's Disease Neuroimaging Initiative data yields covariate-adjusted percentile bands with uncertainty, elucidating how diagnosis changes the distribution along the tract.
Related
- Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel
- Unrestricted conditional maximum likelihood estimation for a Fréchet regression model
- Penalized Likelihood Inference in Highly Multivariate Sparse Spectral Graphical Gaussian Processes
- Joint Conditional Maximum Likelihood Estimation of Beta Regression for Panel Data with Higher-Order Autoregressive Errors
- Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes
BibTeX
@misc{kim2026finiterank,
title = {{Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation}},
author = {Jaehoan Kim and Indrajit Ghosh and Debdeep Pati and Dipankar Bandyopadhyay},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.09452},
eprint = {2610.09452},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.09452},
}
RIS
TY - UNPB
TI - Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
AU - Jaehoan Kim
AU - Indrajit Ghosh
AU - Debdeep Pati
AU - Dipankar Bandyopadhyay
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.09452
UR - https://arxiv.org/abs/2610.09452
AB - Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location. Logistic Gaussian processes (LGP) give a flexible prior for such densities. However, the normalizing constant of an LGP has no closed form, so existing methods approximate or replace the likelihood. We propose the exact likelihood finite-rank LGP (ExFR-LGP), which writes the log density as the sum of two bivariate functions, one of the response and a location-varying linear index of the covariates, and one of the response and the location. Each function is assigned a finite-rank Gaussian process prior that is piecewise linear on a regular grid, enabling the normalizing constant, the conditional mean and the quantiles to enjoy closed form representations. Posterior samples are drawn from the exact posterior under this prior via a Gibbs sampler that updates the Gaussian components by elliptical slice sampling. When the true log density is the sum of two such bivariate functions, we show that the posterior contracts at the minimax rate of an α-smooth bivariate density up to a logarithmic factor. Numerical experiments using synthetic data demonstrate the effectiveness of ExFR-LGP in conditional density estimation. Furthermore, application to fractional anisotropy responses along the corpus callosum in the Alzheimer's Disease Neuroimaging Initiative data yields covariate-adjusted percentile bands with uncertainty, elucidating how diagnosis changes the distribution along the tract.
ER -