gautam-sabnis Goto Github PK

a-divide-and-conquer-strategy-for-high-dimensional-bayesian-factor-models

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional factor models. Our approach distributes the task of high-dimensional covariance matrix estimation to multiple cores, solves each subproblem separately via a latent factor model, and then combines these estimates to produce a globale estimate of the covariance matrix. The MATLAB code is available for public use. The paper is available at https://arxiv.org/pdf/1612.02875.pdf.

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

fecalboli

foofactors

genetic_correlation

heritability

imsm2016

komp-gait-analysis

kompgait

moseq-drugs

mousegwas

An R package for easy execution of mouse GWAS

patchwork

All the Git-it Workshop completers!

practical-python

Practical Python Programming (course by @dabeaz)

resume

unsupervised-clustering

vfi-modeling

visual-frailty-index-analysis

xaringan_gh