R package for adaptive correlation and covariance matrix shrinkage.

Experiments with experimental rule-based models to go along with imodels.

This repository contains data and code relative to the manuscript "A large covariance matrix estimator under intermediate spikiness regimes" by Matteo Farnè and Angela Montanari (https://arxiv.org/abs/1711.08950).

Code for the paper E. Raninen and E. Ollila, "Bias Adjusted Sign Covariance Matrix," in IEEE Signal Processing Letters, vol. 29, pp. 339-343, 2022, doi: 10.1109/LSP.2021.3134940.

A collaborative repository highlighting Bayesian autoregressive analysis with extensions. It is prepared by the students of Macroeconometrics at the University of Melbourne.

Horseshoe regression model fitted in PyMC.

Code for the paper E. Raninen and E. Ollila, “Coupled regularized sample covariance matrix estimator for multiple classes,” in IEEE Transactions on Signal Processing, vol. 69, pp. 5681–5692, 2021, doi: 10.1109/TSP.2021.3118546.

Code for the paper E. Raninen, D. E. Tyler and E. Ollila, "Linear pooling of sample covariance matrices," in IEEE Transactions on Signal Processing, Vol 70, pp. 659-672, 2022, doi: 10.1109/TSP.2021.3139207.

Deformable lattice Boltzmann method for diffusion in 1D moving domains

My Master's thesis on Bayesian Classification with Regularized Gaussian Models

Introduction to Data Mining

Sliding Filter for AWGN Denoising

Word Enrichment Analysis using VEctor Representations

Jackstraw Weighted Shrinkage Methods

R package for Dirichlet adaptive shrinkage and smoothing

Nested Cross-Validation for Bayesian Optimized Linear Regularization

Tool combining pngcrush and optipng to optimize input PNG

Some code related to our paper Per,Duc,Nes. Detection (2019). The objective is to detect block-exchangeable structures in correlation matrices. For any help, please contact me or leave a comment somewhere. I will be glad to help you.

Base saturation percentage determination using shrinkage method. Due to the multicollinearity issue, we chose shrinkage/penalized/regularized regression. Since, we have small number of samples, we had no luxury of having separate test set of data, so we did iterated k-Fold cross validation.

R code and BayesX scripts for effect selection and complexity reduction on basis of the Normal Beta Prime Spike and Slab Prior