Karim Filali, Jeff A. Bilmes
We present a generalization of dynamic Bayesian networks to concisely describe complex probability distributions such as in problems with multiple interacting variable-length streams of random variables. Our framewor k incorporates recent graphical model constructs to account for existence uncert ainty, value-specific independence, aggregation relationships, and local and global constraints, while still retaining a Bayesian network interpretation and effic ient inference and learning techniques. We introduce one such general technique, which is an extension of Value Elimination, a backtracking search inference algo rithm. Multi-dynamic Bayesian networks are motivated by our work on Statistical Machine Translation (MT). We present results on MT word alignment in support of our claim that MDBNs are a promising framework for the rapid prototyping of new MT systems.