Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
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Updated
Jun 21, 2024 - Python
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation
World beating online covariance and portfolio construction.
Implementation of linear CorEx and temporal CorEx.
Lightweight robust covariance estimation in Julia
PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing
Mean and Covariance Matrix Estimation under Heavy Tails
A Python front-end for the large-scale graphical LASSO optimizer BigQUIC (written in R).
Framework for estimating parameters and the empirical sandwich covariance matrix from a set of unbiased estimating equations (i.e. M-estimation) in R.
Unidimensional trivial Kalman filter (header only, Arduino compatible) library
General purpose correlation and covariance estimation
Implementation of the Paper "Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models".
gips - Gaussian model Invariant by Permutation Symmetry
This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI
R code and dataset for the paper on spatially functional data
R Package: Regularized Principal Component Analysis for Spatial Data
A repo for toy examples to test uncertainties estimation of neural networks
Code accompanying the paper "Globally Optimal Learning for Structured Elliptical Losses", published at NeurIPS 2019
R package for Partially Separable Multivariate Functional Data and Functional Graphical Models
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