BayesianGraph

Introduction

This repository contains the coding for Bayesian analysis of non-Gaussian processes on undirected graphs. The codes are summarized in different orders suggesting different functionals in conducting the Bayesian analysis of the non-Gaussian process on undirected graphs.

Import required modules

This can be founded in /modules

Spectral decomposition of graph Laplacian

/Graph_wavelet contains coding to generate graph wavelets in characterizing the spectral structure of undirected graphs.

multi-stage-lasso for priors of variance parameters

In /prior_var, one may find the coding to obtain priors for variance parameters: $\boldsymbol \sigma^2=$ $\sigma^2_0, \sigma^2_1, ..., \sigma^2_J$ and $\tau^2$

MCMC algorithm

/MCMC_analysis includes two files, showing the MCMC sampling algorithm, and chain and prediction performance evaluation.

Example

/test include a sample coding showing the simulation