We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred mot...
The random k-SAT model is extensively used to compare satisfiability algorithms or to find the best settings for the parameters of some algorithm. Conclusions are derived from the...
We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomp...
—The design of new embedded systems is getting more and more complex as more functionality is integrated into these systems. To deal with the design complexity, a predictable des...
Motivation Protein remote homology prediction and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines a...