Collective Inference on Markov Models for Modeling Bird Migration
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 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.
Cite
Text
Elmohamed et al. "Collective Inference on Markov Models for Modeling Bird Migration." Neural Information Processing Systems, 2007.Markdown
[Elmohamed et al. "Collective Inference on Markov Models for Modeling Bird Migration." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/elmohamed2007neurips-collective/)BibTeX
@inproceedings{elmohamed2007neurips-collective,
title = {{Collective Inference on Markov Models for Modeling Bird Migration}},
author = {Elmohamed, M.a. S. and Kozen, Dexter and Sheldon, Daniel R.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {1321-1328},
url = {https://mlanthology.org/neurips/2007/elmohamed2007neurips-collective/}
}