On Bayesian Network Approximation by Edge Deletion
This addresses model simplification for probabilistic inference practitioners, but appears incremental as it builds on existing edge deletion approaches.
The paper tackles the problem of simplifying Bayesian networks for probabilistic inference by deleting edges based on available evidence, providing theoretical bounds on KL-divergence and demonstrating empirical promise for approximate inference.
We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and approximate networks, which highlight the impact of given evidence on the quality of approximation and shed some light on good and bad candidates for edge deletion. We finally demonstrate empirically the promise of the proposed edge deletion technique as a basis for approximate inference.