<p>One of the primary challenges in graphical models is inference, or re-constructing a marginal probability from the graphical model's factorized representation. While tractable for some graphs, the cost of inference grows exponentially with the graphical model's complexity, necessitating approximation for more complex graphs. The leaky join algorithm couples together incremental view maintenance with approximate query processing to produce an anytime algorithm for inference in a very large-scale Bayes net.</p>