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
World beating online covariance and portfolio construction.
gips - Gaussian model Invariant by Permutation Symmetry
Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation
Framework for estimating parameters and the empirical sandwich covariance matrix from a set of unbiased estimating equations (i.e. M-estimation) in R.
Lightweight robust covariance estimation in Julia
Implementation of the Paper "Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models".
R Package: Regularized Principal Component Analysis for Spatial Data
General purpose correlation and covariance estimation
Mean and Covariance Matrix Estimation under Heavy Tails
PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing
A few statistical methods appropriate for applications in the biological and social sciences.
Implementation of linear CorEx and temporal CorEx.
A repo for toy examples to test uncertainties estimation of neural networks
Different optimization algorithms like Hill climbing, Simulated annealing, Late accepted Hill climbing , Genetic Algorithm is implemented from scratch.
R package for Partially Separable Multivariate Functional Data and Functional Graphical Models
Unidimensional trivial Kalman filter (header only, Arduino compatible) library
Additive Covariance Modeling via Unconstrained Parametrization
Fast Bayesian Inference in Large Graphical Models
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