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Countering the Excessive Subpoena for Scholarly Research
A researcher has many opportunities to safeguard research and take a stance in court to protect the privacy of study participants in the interest of well-grounded scientific or social analysis
Countering the Excessive Subpoena for Scholarly Research
A researcher has many opportunities to safeguard research and take a stance in court to protect the privacy of study participants in the interest of well-grounded scientific or social analysis
Spartan Daily, November 18, 1971
Volume 59, Issue 37https://scholarworks.sjsu.edu/spartandaily/5536/thumbnail.jp
Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
We present a hierarchical Bayesian method for atmospheric trace gas
inversions. This method is used to estimate emissions of trace gases as well
as "hyper-parameters" that characterize the probability density functions
(PDFs) of the a priori emissions and model-measurement covariances. By
exploring the space of "uncertainties in uncertainties", we show that the
hierarchical method results in a more complete estimation of emissions and
their uncertainties than traditional Bayesian inversions, which rely heavily
on expert judgment. We present an analysis that shows the effect of
including hyper-parameters, which are themselves informed by the data, and
show that this method can serve to reduce the effect of errors in assumptions
made about the a priori emissions and model-measurement uncertainties. We
then apply this method to the estimation of sulfur hexafluoride (SF6)
emissions over 2012 for the regions surrounding four Advanced Global
Atmospheric Gases Experiment (AGAGE) stations. We find that improper
accounting of model representation uncertainties, in particular, can lead to
the derivation of emissions and associated uncertainties that are unrealistic
and show that those derived using the hierarchical method are likely to be
more representative of the true uncertainties in the system. We demonstrate
through this SF6 case study that this method is less sensitive to
outliers in the data and to subjective assumptions about a priori emissions
and model-measurement uncertainties than traditional methods
Spartan Daily, May 3, 1999
Volume 112, Issue 61https://scholarworks.sjsu.edu/spartandaily/9418/thumbnail.jp
Spartan Daily, February 15, 1990
Volume 94, Issue 14https://scholarworks.sjsu.edu/spartandaily/7944/thumbnail.jp
Spartan Daily, February 15, 1990
Volume 94, Issue 14https://scholarworks.sjsu.edu/spartandaily/7944/thumbnail.jp
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