Abstract
A Multiregression Dynamic Model (MDM) is a class of multivariate
time series that represents various dynamic causal processes in a graphical way.
One of the advantages of this class is that, in contrast to many other Dynamic
Bayesian Networks, the hypothesised relationships accommodate conditional conjugate
inference. We demonstrate for the first time how straightforward it is to
search over all possible connectivity networks with dynamically changing intensity
of transmission to find the Maximum a Posteriori Probability (MAP) model
within this class. This search method is made feasible by using a novel application
of an Integer Programming algorithm. The efficacy of applying this particular
class of dynamic models to this domain is shown and more specifically the computational
efficiency of a corresponding search of 11-node Directed Acyclic Graph
(DAG) model space. We proceed to show how diagnostic methods, analogous to
those defined for static Bayesian Networks, can be used to suggest embellishment
of the model class to extend the process of model selection. All methods are illustrated
using simulated and real resting-state functional Magnetic Resonance
Imaging (fMRI) data.
time series that represents various dynamic causal processes in a graphical way.
One of the advantages of this class is that, in contrast to many other Dynamic
Bayesian Networks, the hypothesised relationships accommodate conditional conjugate
inference. We demonstrate for the first time how straightforward it is to
search over all possible connectivity networks with dynamically changing intensity
of transmission to find the Maximum a Posteriori Probability (MAP) model
within this class. This search method is made feasible by using a novel application
of an Integer Programming algorithm. The efficacy of applying this particular
class of dynamic models to this domain is shown and more specifically the computational
efficiency of a corresponding search of 11-node Directed Acyclic Graph
(DAG) model space. We proceed to show how diagnostic methods, analogous to
those defined for static Bayesian Networks, can be used to suggest embellishment
of the model class to extend the process of model selection. All methods are illustrated
using simulated and real resting-state functional Magnetic Resonance
Imaging (fMRI) data.
| Original language | English |
|---|---|
| Pages (from-to) | 441-478 |
| Number of pages | 38 |
| Journal | Bayesian Analysis |
| Volume | 10 |
| Issue number | 2 |
| Early online date | 2 Feb 2015 |
| DOIs | |
| Publication status | Published - 1 Jun 2015 |
