Numerical possibility distributions can encode special convex families of probability measures. The connection between possibility theory and probability theory is potentially fru...
In complex multi-agent fusion systems resource conflicts are very likely to occur. We propose an algorithm that determines the optimal sensing resource to fusion task assignment,...
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of t...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-...