Logan Perreault

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The use of phase-type distributions is an established method for extending the representational power of continuous time Bayesian networks beyond exponentially-distributed state transitions. In this paper, we propose a method for learning phase-type distributions from known parametric distributions. We find that by using particle swarm optimization to(More)
The use of swarm robotics in search tasks is an active area of research. A variety of algorithms have been developed that effectively direct robots toward a desired target by leveraging their collaborative sensing capabilities. Unfortunately, these algorithms often neglect the task of communicating possible task solutions outside of the swarm. Many(More)
A Bayesian method is presented for the analysis of two types of sudden change at an unknown time-point in a sequence of energy inflows modeled by independent normal random variables. First, the case of a single shift in the mean level is revisited to show how such a problem can be straightforwardly addressed through the Bayesian framework. Second, a change(More)
—In this paper we present a proposal for a new prognostic model to be included in a future revision of the IEEE Std 1232-2010 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). Specifically, we introduce the continuous time Bayesian network (CTBN) as an alternative to the previously proposed dynamic Bayesian(More)
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