Performance of Variable Selection Methods in Regression Using Variations of the Bayesian Information Criterion

Burr, Tom; Fry, Herbert A.; McVey, B.D.; Sander, Eric L.; Cavanaugh, Joseph E.; Neath, Andrew A.

doi:10.1080/03610910701812428

Cited by 13 publications

(19 citation statements)

References 14 publications

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“…An iterative, inverse problem algorithm is then needed that searches for the solution that produces the best overall fit to the observable facility data. Model averaging is an extension of this strategy that might also be considered (Burr et al, 2008). In the example above, perhaps the shielding is known to be either iron or lead with a measured thickness, so the gamma attenuation correction would be applied for either iron or lead.…”

Section: Problem Formulationmentioning

confidence: 99%

Integration of facility modeling capabilities for nuclear nonproliferation analysis

García¹,

Burr²,

Coles³

et al. 2012

Progress in Nuclear Energy

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Developing automated methods for data collection and analysis that can facilitate nuclear nonproliferation assessment is an important research area with significant consequences for the effective global deployment of nuclear energy. Facility modeling that can integrate and interpret observations collected from monitored facilities in order to ascertain their functional details will be a critical element of these methods. Although improvements are continually sought, existing facility modeling tools can characterize all aspects of reactor operations and the majority of nuclear fuel cycle processing steps, and include algorithms for data processing and interpretation. Assessing nonproliferation status is challenging because observations can come from many sources, including local and remote sensors that monitor facility operations, as well as open sources that provide specific business information about the monitored facilities, and can be of many different types. Although many current facility models are capable of analyzing large amounts of information, they have not been integrated in an analyst-friendly manner. This paper addresses some of these facility modeling capabilities and illustrates how they could be integrated and utilized for nonproliferation analysis. The inverse problem of inferring facility conditions based on collected observations is described, along with a proposed architecture and computer framework for utilizing facility modeling tools. After considering a representative sampling of key facility modeling capabilities, the proposed integration framework is illustrated with several examples. Highlights: We consider integration of facility modeling into a tool for proliferation analysis.  We describe the inverse problem of inferring the facility based on observables.  We propose architecture for integrating facility models into a suitable framework.  We illustrate the proposed integration framework with several examples.

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Section: Problem Formulationmentioning

confidence: 99%

Integration of facility modeling capabilities for nuclear nonproliferation analysis

García¹,

Burr²,

Coles³

et al. 2012

Progress in Nuclear Energy

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“…These probability estimates are impacted by nonGaussian behavior [23,41], implying that although the BMA outputs are in the 0 to 1 range, they do not necessarily behave as well-calibrated probabilities [41]. Here we briefly describe BMA for subset selection.…”

Section: Bayesian Model Averagingmentioning

confidence: 99%

Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery

Burr

Hengartner

2006

Sensors

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Abstract:The performance of weak gaseous plume-detection methods in hyperspectral long-wave infrared imagery depends on scene-specific conditions such at the ability to properly estimate atmospheric transmission, the accuracy of estimated chemical signatures, and background clutter. This paper reviews commonly-applied physical models in the context of weak plume identification and quantification, identifies inherent error sources as well as those introduced by making simplifying assumptions, and indicates research areas.

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“…The latter is also suitable for mixed spectral problems, which are addressed by unmixing techniques and material identification through library spectra association. Typical approaches are inspired by existing methods (e.g., [94][95][96] introductory subjects, including spectral variability, subpixel targets modelling, likelihood detectors, matched filters and ROC-based performance analysis [92]. While FAM [93] is considered a promising approach to deal with the aforementioned issue related with false alarm rates, TID is useful to determine if a given pixel within a certain detector contains or not the target.…”

Section: Subpixel Target Detectionmentioning

confidence: 99%

Section: Subpixel Target Detectionmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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Performance of Variable Selection Methods in Regression Using Variations of the Bayesian Information Criterion

Cited by 13 publications

References 14 publications

Integration of facility modeling capabilities for nuclear nonproliferation analysis

Integration of facility modeling capabilities for nuclear nonproliferation analysis

Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

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