Recursive Nearest Neighbor Co-Kriging Models for Big Multiple Fidelity Spatial Data Sets

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co-kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing and convergence because they require the simulation of high-dimensional random effect vectors from their posteriors given large datasets. To enable fast inference in big data spatial problems, we propose the recursive nearest neighbor co-kriging (RNNC) model. Based on this model, we develop two computationally efficient inferential procedures: a) the collapsed RNNC which reduces the posterior sampling space by integrating out the latent processes, and b) the conjugate RNNC, an MCMC free inference which significantly reduces the computational time without sacrificing prediction accuracy. An important highlight of conjugate RNNC is that it enables fast inference in massive multifidelity data sets by avoiding expensive integration algorithms. The efficient computational and good predictive performances of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High-resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes.Comment: arXiv admin note: text overlap with arXiv:2004.0134

Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing and convergence because they require the simulation of high‐dimensional random effect vectors from their posteriors given large datasets. To enable fast inference in big data spatial problems, we propose the recursive nearest neighbor co‐kriging (RNNC) model. Based on this model, we develop two computationally efficient inferential procedures: (a) the collapsed RNNC which reduces the posterior sampling space by integrating out the latent processes, and (b) the conjugate RNNC, an MCMC free inference which significantly reduces the computational time without sacrificing prediction accuracy. An important highlight of conjugate RNNC is that it enables fast inference in massive multifidelity data sets by avoiding expensive integration algorithms. The efficient computational and good predictive performances of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High‐resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes

Male mastodon landscape use changed with maturation (late Pleistocene, North America)

Under harsh Pleistocene climates, migration and other forms of seasonally patterned landscape use were likely critical for reproductive success of mastodons (Mammut americanum) and other megafauna. However, little is known about how their geographic ranges and mobility fluctuated seasonally or changed with sexual maturity. We used a spatially explicit movement model that coupled strontium and oxygen isotopes from two serially sampled intervals (5+ adolescent years and 3+ adult years) in a male mastodon tusk to test for changes in landscape use associated with maturation and reproductive phenology. The mastodon’s early adolescent home range was geographically restricted, with no evidence of seasonal preferences. Following inferred separation from the matriarchal herd (starting age 12 y), the adolescent male’s mobility increased as landscape use expanded away from his natal home range (likely central Indiana). As an adult, the mastodon’s monthly movements increased further. Landscape use also became seasonally structured, with some areas, including northeast Indiana, used only during the inferred mastodon mating season (spring/summer). The mastodon died in this area (\u3e150 km from his core, nonsummer range) after sustaining a craniofacial injury consistent with a fatal blow from a competing male’s tusk during a battle over access to mates. Northeast Indiana was likely a preferred mating area for this individual and may have been regionally significant for late Pleistocene mastodons. Similarities between mammutids and elephantids in herd structure, tusk dimorphism, tusk function, and the geographic component of male maturation indicate that these traits were likely inherited from a common ancestor

Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants

This report describes the status of ongoing research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions