: While the common kinds of uncertainties in databases (e.g., null values, disjunction, corrupt/missing data, domain mismatch, etc.) have been extensively studied, a relatively une...
Markov Decision Processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given...
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
The problem of "time separation" can be stated as follows: Given a system made of several connected components, each one entailing a local delay known with uncertainty, ...
Probabilistic Boolean networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the netw...