Bayesian inference and conditional probabilities as performance metrics for homeland security sensors

SPUX - a Scalable Package for Bayesian Uncertainty Quantification

10.5194/egusphere-egu2020-9834 ◽

2020 ◽

Author(s):

Jonas Sukys ◽

Marco Bacci

Keyword(s):

Bayesian Inference ◽

Uncertainty Quantification ◽

High Performance ◽

Performance Metrics ◽

Expert Knowledge ◽

Simple Random Walk ◽

Scientific Workflow ◽

Model Parameters ◽

Parameter Uncertainties ◽

Computational Performance

<div> <div>SPUX (Scalable Package for Uncertainty Quantification in "X") is a modular framework for Bayesian inference and uncertainty quantification. The SPUX framework aims at harnessing high performance scientific computing to&#160;tackle&#160;complex&#160;aquatic&#160;dynamical&#160;systems&#160;rich&#160;in&#160;intrinsic&#160;uncertainties,</div> <div>such as ecological ecosystems, hydrological catchments, lake dynamics, subsurface flows, urban floods, etc. The challenging task of quantifying input, output and/or parameter uncertainties in such stochastic models is tackled using Bayesian inference techniques, where numerical sampling and filtering algorithms assimilate prior expert knowledge and available experimental data. The SPUX framework greatly simplifies uncertainty quantification for realistic computationally costly models and provides an accessible, modular, portable, scalable, interpretable and reproducible scientific workflow. To achieve this, SPUX can be coupled to any serial or parallel model written in any programming language (e.g. Python, R, C/C++, Fortran, Java), can be installed either on a laptop or on a parallel cluster, and has built-in support for automatic reports, including algorithmic and computational performance metrics. I will present key SPUX concepts using a simple random walk example, and showcase recent realistic applications for catchment and lake models. In particular, uncertainties in model parameters, meteorological inputs, and data observation processes are inferred by assimilating available in-situ and remotely sensed datasets.</div> </div>

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Hilbertian sine as an absolute measure of Bayesian inference in ISR, homeland security, medicine, and defense

10.1117/12.2220832 ◽

2016 ◽

Author(s):

Tomasz Jannson ◽

Wenjian Wang ◽

Juan Hodelin ◽

Thomas Forrester ◽

Volodymyr Romanov ◽

...

Keyword(s):

Bayesian Inference ◽

Homeland Security ◽

Absolute Measure

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Radiation detector materials: An overview

Journal of Materials Research ◽

10.1557/jmr.2008.0319 ◽

2008 ◽

Vol 23 (10) ◽

pp. 2561-2581 ◽

Cited By ~ 151

Author(s):

B.D. Milbrath ◽

A.J. Peurrung ◽

M. Bliss ◽

W.J. Weber

Keyword(s):

Homeland Security ◽

Performance Metrics ◽

Materials Science ◽

Radiation Detector ◽

Radiation Detection ◽

National Defense ◽

Challenges And Opportunities ◽

Science Community ◽

Faster Development ◽

Detector Materials

Due to events of the past two decades, there has been new and increased usage of radiation-detection technologies for applications in homeland security, nonproliferation, and national defense. As a result, there has been renewed realization of the materials limitations of these technologies and greater demand for the development of next-generation radiation-detection materials. This review describes the current state of radiation-detection material science, with particular emphasis on national security needs and the goal of identifying the challenges and opportunities that this area represents for the materials-science community. Radiation-detector materials physics is reviewed, which sets the stage for performance metrics that determine the relative merit of existing and new materials. Semiconductors and scintillators represent the two primary classes of radiation detector materials that are of interest. The state-of-the-art and limitations for each of these materials classes are presented, along with possible avenues of research. Novel materials that could overcome the need for single crystals will also be discussed. Finally, new methods of material discovery and development are put forward, the goal being to provide more predictive guidance and faster screening of candidate materials and thus, ultimately, the faster development of superior radiation-detection materials.

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