I am designing a Dynamic Bayesian Network, but I am a little confused about some definition of DBN and Markov network. In my network, the edges from the hidden nodes of last frame to the current frame are directed as normal. And the edges from the oberservation nodes to the hidden nodes are directed. But I want to set the connections among all the hidden nodes undirected, because I want to implement something like data fusion on them. So, the questions are: 1. What is such network? Can it be named as DBN or markov network? 2. What is the most efficient inference method for such network?
http://en.wikipedia.org/wiki/Bayesian_network: "A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG)."
A DBN consist of directed connections and so will not suit your needs.