Independence of Causal Influence and Clique Tree Propagation
This work addresses computational bottlenecks in exact inference for Bayesian networks, offering incremental improvements for researchers and practitioners in probabilistic graphical models.
The paper tackles the problem of improving efficiency in Bayesian network inference by exploiting independence of causal influence (ICI) factorization within clique tree propagation (CTP), resulting in a new algorithm that is significantly more efficient than previous methods.
This paper explores the role of independence of causal influence (ICI) in Bayesian network inference. ICI allows one to factorize a conditional probability table into smaller pieces. We describe a method for exploiting the factorization in clique tree propagation (CTP) - the state-of-the-art exact inference algorithm for Bayesian networks. We also present empirical results showing that the resulting algorithm is significantly more efficient than the combination of CTP and previous techniques for exploiting ICI.