אפשרות לוגיסטית גאוסיאנית לדחיסת צפיפות עם סימולציה קינטית של Langevin

תקציר מקורי באנגליתarXiv:2610.09591v1 Announce Type: cross Abstract: Conditional density estimation targets the full distribution of a response given covariates, as required, for example, for per-galaxy photometric redshifts. We develop a scalable Bayesian estimator based on the logistic Gaussian process. The log conditional density has a separable covariance: a Mat\'ern kernel along the response, represented in a truncated Fourier basis on a circle, and a covariate kernel represented by Nystr\"om features, which accommodate non-stationary kernels with input-dependent amplitudes and length scales. Instead of a Laplace or variational approximation, we sample the latent field of this finite-feature model. Given the hyperparameters, its posterior is strongly log-concave with a uniformly bounded Hessian, and we