A novel method for nonparametric density estimation from weighted samples is proposed, represented in discretized form as a regular grid of square roots of probabilities or density values, and achieves smoothness with an additional Fisher Information regularization.
The SLGP package provides nonparametric estimation of fields of probability densities indexed by covariates, from unevenly sized samples with few or no replicates. A Spatial Logistic Gaussian Process applies a logistic density transformation to a finite-rank Gaussian process over a joint index-response domain, yielding...
Density ratios quantify distribution shift from a probability-mass point of view, whereas displacement fields describe, from a dynamical point of view, how one distribution is transported onto another. Although both offer complementary insights, they are usually estimated separately, and converting one into the other r...
Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based on Bayesian generative modeling. BayesNDE learns a Bayesian generative model and evaluates its density without requiring invertible networks or Jacobian-determinant compu...
Kernel density estimation (KDE) is a widely used nonparametric tool for recovering unknown probability density functions without strong distributional assumptions. However, even well-tuned kernel density estimators often exhibit non-negligible bias, which can potentially affect the accuracy of subsequent statistical in...
Wen-shuenn Deng, Yi-Chen Lin, Kung-Han Yang et al.· International Journal of Sta...· 0 citations
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 approxima...
Jae-Hyun Kim, Indrajit Ghosh, D. Pati et al.· 1 citation
Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and computational modelling. This process is critical for deriving interpretable and generalizable relationships characterizing the underlying phenomenon. Traditionally, thi...
V. Kanaujia, Riyansha Singh, Shashank Sharma et al.· 0 citations
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