BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks

Gaussian processes for Bayesian analysis User guide for Matlab toolbox

Gaussian processes in Bayesian modeling : Manual for Matlab toolbox

(This is an early version of the manual, which is still subject to some modifications. The text contains still errors in some details but great picture is correctly described.)

Non-Parametric Bayesian Sum-Product Networks

We define two non-parametric models for Sum-Product Networks (SPNs) (Poon & Domingos, 2011). The first is a tree structure of Dirichlet Processes; the second is a dag of hierarchical Dirichlet Processes. These generative models for data implicitly define a prior distribution on SPN of tree and of dag structure. They allow MCMC fitting of data to SPN models, and the learning of SPN structure fro...

Non-parametric Bayesian modeling of complex networks

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infini...

MATLAB toolbox for functional connectivity

The term "functional connectivity" is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. Yet these relationships have been quantified using very different measures and the ext...