Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes...
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, sc...
Leonardo Chang, Miriam Monica Duarte, Luis Enrique...
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserve...
We represent switching activity in VLSI circuits using a graphical probabilistic model based on Cascaded Bayesian Networks (CBN’s). We develop an elegant method for maintaining ...