Publications

Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar

Authors:
SHELDON, D.; FARNSWORTH, A.; IRVINE, J.; VAN DOREN, B.; WEBB, K.; DIETTERICH, T.; and KELLING, S.
Date:
2013
Publication:
AAAI Conference on Artificial Intelligence, North America, jun. 2013.
Abstract:
Archived data from the WSR-88D network of weather radars in the US hold detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We present an approximate Bayesian inference algorithm to reconstruct the velocity fields of birds migrating in the vicinity of a radar station. This is part of a larger project to quantify bird migration at large scales using weather radar data.
Download:
Approximate-Bayesian-Inference-for-Reconstructing-Velocities-of-Migrating-Birds-from-Weather-Radar.pdf
Online:
http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6468

Collective Graphical Models

Authors:
Sheldon, D., and T.G. Dietterich
Date:
2012
Publication:
Neural Information Processing Systems (NIPS), Grenada, Spain 12-15 December 2011
Abstract:
There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregate information (counts or low-dimensional contingency tables). This paper introduces Collective Graphical Models—a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model. We derive a highly-efficient Gibbs sampling algorithm for sampling from the posterior distribution of the sufficient statistics conditioned on noisy aggregate observations, prove its correctness, and demonstrate its effectiveness experimentally.
Download:
Collective-Graphical-Models.pdf
Online:
http://people.cs.umass.edu/~sheldon/papers/cgm-single-file.pdf
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