We propose an approach for timing analysis of software-based embedded computer systems that builds on the established probabilistic framework of Bayesian networks. We envision an ...
People using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals ...
We propose LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY ar...
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...