21 citations · 27 across the 2 of their papers we have counts for
2 papers
cs.AI2012★ 6 cited
Efficient inference in persistent Dynamic Bayesian Networks
Tomas Singliar, Denver Dash
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interven…
cs.AI2012★ 21 cited
Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek J. Druzdzel
In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across tim…