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Title: Collective Inference on Markov Models for Modeling Bird Migration
Authors: Sheldon, Daniel
Elmohamed, M. A. Saleh
Kozen, Dexter
Keywords: computer information science
technical report
Artificial Intelligence
Issue Date: 18-May-2007
Publisher: Cornell University
Abstract: We investigate a family of inference problems on Markov models, where many sample paths are drawn from a Markov chain and partial information is revealed to an observer who attempts to reconstruct the sample paths. We present algorithms and hardness results for several variants of this problem which arise by revealing different information to the observer and imposing different requirements for the reconstruction of sample paths. Our algorithms are analogous to the classical Viterbi algorithm for Hidden Markov Models, which finds single most probable sample path given a sequence of observations. Our work is motivated by an important application in ecology: inferring bird migration paths from a large database of observations.
Appears in Collections:Computing and Information Science Technical Reports

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