期刊
STATISTICAL METHODS AND APPLICATIONS
卷 17, 期 2, 页码 145-165出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10260-007-0077-1
关键词
hierarchical Bayesian analysis; importance sampling; Markov chain Monte Carlo
Studies of the behaviors of glaciers, ice sheets, and ice streams rely heavily on both observations and physical models. Data acquired via remote sensing provide critical information on geometry and movement of ice over large sections of Antarctica and Greenland. However, uncertainties are present in both the observations and the models. Hence, there is a need for combining these information sources in a fashion that incorporates uncertainty and quantifies its impact on conclusions. We present a hierarchical Bayesian approach to modeling ice-stream velocities incorporating physical models and observations regarding velocity, ice thickness, and surface elevation from the North East Ice Stream in Greenland. The Bayesian model leads to interesting issues in model assessment and computation.
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