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

1996 Virginian

https://digitalcommons.longwood.edu/yearbooks/1045/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