Poisson Processes

Tuesday, April 24, 2018 - 3:30pm - 4:00pm
Rebecca Willett (University of Wisconsin, Madison)
Consider observing a collection of discrete events within a network that reflects how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the past events.
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