Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)
M.a. Elmohamed, Dexter Kozen, Daniel R. Sheldon
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 algo- rithms and hardness results for several variants of this problem which arise by re- vealing different information to the observer and imposing different requirements for the reconstruction of sample paths. Our algorithms are analogous to the clas- sical Viterbi algorithm for Hidden Markov Models, which ﬁnds the 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.