Probabilistic inference problems arise naturally in distributed
systems. For example, robots in a team may combine local laser range
scans to build a global map of the environment; sensors in an
emergency response deployment may collect local temperature
measurements to anticipate the spread of fire. By distributing the
computation across several devices, sensor networks offer a
fundamentally different computational medium . one where the nodes
need to communicate with each other, in order to exchange information.
This medium imposes new requirements on probabilistic inference: for
example, even if some of the nodes fail, and the information they
carry is lost, the rest of the nodes should still be able to recover a
principled approximation of the distribution.
In my talk, I will discuss fundamental aspects of probabilistic inference in distributed systems and outline algorithms that perform robustly in this more stringent setting. One key idea is to represent the prior information as a set of marginals that are carried redundantly by the nodes of the network; if a node fails, the remaining nodes can still compute a KL projection of the true distribution. I will consider both the static and the dynamic settings, and show results on applications from real sensor network deployments.
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Pradeep Ravikumar Last modified: Fri May 4 22:00:24 EDT 2007