22 skills found
brainets / FritesFramework for Information Theoretical analysis of Electrophysiological data and Statistics
robince / GcmiFunctions for calculating mutual information and other information theoretic quantities using a parametric Gaussian copula.
udellgroup / GcimputeMissing value imputation using Gaussian copula
AmirhosseinHonardoust / Synthetic Data ArtistA professional, research-grade comparison of Gaussian Copula and Variational Autoencoder (VAE) methods for synthetic tabular data generation. Includes full evaluation pipeline with distribution overlap, correlation analysis, PCA projections, pairplots, metrics, and automated visual reports.
Laouen / THOITHOI: An efficient library for higher order interactions analysis based on Gaussian copulas enhanced by batch-processing
winstonll / SynCSupplementary material for paper "SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula"
shaobohan / VariationalGaussianCopulaMatlab Code for Variational Gaussian Copula Inference
cuiruifei / CopulaFactorModelInference for Gaussian copula factor models and its application to causal discovery.
AEBilgrau / GMCMUnsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models
boennecd / MdgcProvides functions to impute missing values using Gaussian copulas for mixed data types.
Zhuosd / OLDSDThis paper explores a new online learning problem where the data streams are generated from an over-time varying feature space, in which the random variables are of mixed data types including Boolean, ordinal, and continuous. The crux of this setting lies in how to establish the relationship among features, such that the learner can enjoy 1) reconstructed information of the missed-out old features and 2) a jump-start of learning new features with educated weight initialization. Unfortunately, existing methods mainly assume a linear mapping relationship among features or that the multivariate joint distribution could be modeled as Gaussians, limiting their applicability to the mixed data types. To fill the gap, we in this paper propose to model the complex joint distribution underlying mixed data with Gaussian copula, where the observed features with arbitrary marginals are mapped onto a latent normal space. The feature correlation is approximated in the latent space through an online EM process. Two base learners trained on the observed and latent features are ensembled to expedite convergence, thereby minimizing prediction risk in an online learning regime. Theoretical and empirical studies substantiate the effectiveness of our proposed approach. Code is available online at https://github.com/Zhuosd/OLDSD.git
udellgroup / GcimputeRMissing value imputation using Gaussian copula
MVL-Lab / TGC MTSThe official implementation of BIBM 24' Paper "Temporal Gaussian Copula For Clinical Multivariate Time Series Data Interpolation"
david-dunson / Gaussian Copula Factor Model"Bayesian Gaussian Copula Factor Models for Mixed Data" by Jared S. Murray, David B. Dunson, Lawrence Carin, Joseph E. Lucas. This is a read-only mirror of the CRAN R package repository.
BiomedDAR / Copula TabularGenerate tabular synthetic data using Gaussian copulas
yincheng / GcvarCode base for Gaussian Copula Variational Inference
lopezpaz / Gaussian Process Conditional CopulasCode for "Gaussian process vine copulas for multivariate dependence" (ICML 2013).
h3ik0th / MC CopulasPython Monte Carlo simulation with Gaussian copulas and SciPy
chenlincigit / TGC MTSTemporal Gaussian Copula For Clinical MTS
ashiq24 / CopulaA python library for sampling and generating new Data points by multivariate Gaussian copulas.
