My Master's thesis on Bayesian Classification with Regularized Gaussian Models
-
Updated
Dec 27, 2015 - R
My Master's thesis on Bayesian Classification with Regularized Gaussian Models
Covariance and correlation matrix via Rhadoop (rmr2 and HDFS)
Covariance Estimation and Denoising for Cryo-EM Images (Covariance Wiener Filtering)
An R package for testing high-dimensional covariance matrices
Algorithms for feature selection based on covariance matrix.
A Python front-end for the large-scale graphical LASSO optimizer BigQUIC (written in R).
SCFGP: Sparsely Correlated Fourier Features Based Gaussian Process
Performing the Financial Analysis on Historic Stock Market Data such as calculating various risks, returns,etc.
JED is a program for performing Essential Dynamics of protein trajectories written in Java. JED is a powerful tool for examining the dynamics of proteins from trajectories derived from MD or Geometric simulations. Currently, there are two types of PCA: distance-pair and Cartesian, and three models: COV, CORR, and PCORR.
Covariance/Correlation for big data in base R
Penalized precision matrix estimation
This R package is a wrapper around the popular "glasso" package with built-in cross validation and visualizations
Penalized precision matrix estimation via block-wise coordinate descent (graphical lasso)
Penalized precision matrix estimation via ADMM
Shrinking characteristics of precision matrix estimators
This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI
Construct portfolios along mean-variance efficient frontier
Finding Covariance Matrix, Correlation Coefficient, Euclidean and Mahalanobis Distance
Add a description, image, and links to the covariance-matrix topic page so that developers can more easily learn about it.
To associate your repository with the covariance-matrix topic, visit your repo's landing page and select "manage topics."