A Collection of Utilities for Modeling Multivariate Data Using Probabilistic Graphical Models
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Updated
Nov 6, 2017 - C++
A Collection of Utilities for Modeling Multivariate Data Using Probabilistic Graphical Models
Computational Studies of Adja Magatte Fall Internship
Machine Learning 2017 / "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", / https://cran.r-project.org/web/packages/simule/
AISTAT 2017 Paper: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
GGM structure learning using 1 bit.
High-dimensional change point detection in Gaussian Graphical models with missing values
Infers species direct association networks
PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing
Source code for the paper "Fast and Accurate Inference of Gene Regulatory Networks through Robust Precision Matrix Estimation", by Passemiers et al.
Linear Gaussian Bayesian Networks - Inference, Parameter Learning and Representation. 🖧
Monte Carlo Penalty Selection for graphical lasso
A Lightning-fast algorithm for Gene Regulatory Network inference from gene expression data
Bayesian structure learning and classification in decomposable graphical models.
This aim of this project is to analyze globular star clusters in the Milky Way, in order to understand their dynamics. The conducted study examined the properties that affect the central velocity dispersion, their impact and the correlations between them.
Scikit-learn compatible estimation of general graphical models
Bayesian Gaussian Graphical Models
This module is a tool for calculating correlations such as Partial, Tetrachoric, Intraclass correlation coefficients, Bootstrap agreement, Rater reliability, Generalizability Theory, Analytic Hierarchy Process, and allows users to produce Gaussian Graphical Model and Partial plot.
🔗 Methods for Correlation Analysis
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