Characterizing the standardization properties of type ia supernovae in the z band with hierarchical Bayesian modelling
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Erin E Hayes↗
SEARCH · Engineering Papers
Results for “Bayesian hierarchical model”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Hierarchical Bayesian models for small area estimation of forest variables using LiDAR
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Neil R. Ver Planck↗
Assessing the impact of fine particulate matter (PM 2.5 ) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates
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Stephanie A. Weber↗
A novel approach to quantifying the sensitivity of current and future cosmological datasets to the neutrino mass ordering through Bayesian hierarchical modeling
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Martina Gerbino↗
Territory and Population Attributes Affect Florida Scrub-Jay Fecundity in Fire Adapted Ecosystems
Fecundity, the number of young produced by a breeding pair during a breeding season, is a primary component in evolutionary and ecological theory and applications. Fecundity can be influenced by many environmental factors and requires long-term study due to the range of variation in ecosystem dynamics. Fecundity data often include a large proportion of zeros when many pairs fail to produce any young during a breeding season due to nest failure or when all young die independently after fledging. We conducted color banding and monthly censuses of Florida scrub-jays (Aphelocoma coerulescens) across 31 years, 15 populations, and 761 territories along central Florida’s Atlantic coast. We quantified how fecundity (juveniles/pair-year) was influenced by habitat quality, presence/absence of nonbreeders, population density, breeder experience, and rainfall, with a zero-inflated Bayesian hierarchical model including both a Bernoulli (e.g., brood success) and a Poisson (counts of young) submodel, and random effects for year, population, and territory. The results identified the importance of increasing “strong” quality habitat, which was a mid-successional state related to fire frequency and extent, because strong territories, and the proportion of strong territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity. Territory size, population density, breeder experience, and rainfall surprisingly had no or small effects. Different mechanisms appeared to cause annual variation in fecundity, as estimates of random effects were not correlated between the success and count submodels. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified empirical research needed to understand how the proportion of low-quality habitats influences population recovery and sustainability, because dispersal into low-quality habitats can drain nonbreeders from strong territories and decrease overall fecundity. We also describe how long term study resulted in reversals in our understanding because of complications involving habitat quality, sociobiology, and population density.
Long-term studies↗
Astrophysical Model Selection in Gravitational Wave Astronomy
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.
hierarchical↗
Interpolating Fields of Carbon Monoxide Data Using a Hybrid Statistical-Physical Model
Atmospheric Carbon Monoxide (CO) is a pollutant gas of which the US congress has mandated regular monitoring, and satellite sensors can be used to retrieve regional concentrations of CO over several vertical layers. However, CO at cloudy locations cannot be observed and have to be estimated from the observed data set, resulting in an interpolation problem. The current state-of-the-art solution is to combine prior information, computed by a deterministic physical model, with observations. However, the deterministic model may introduce uncertainties that do not derive from the data. While sharing certain features with the physical model, this paper presents a Bayesian hierarchical model for interpolating CO on a 3-dimensional spatial grid, across time. To our knowledge such a model has not been considered before. The model is applied to a hypothetical air-quality monitoring scenario, and is compared to existing interpolation methods. The results provide motivation for the use of the statistical model for regional to local applications.
Arellano, A. A.↗
Survival of Juvenile Florida Scrub-Jays is Habitat Specific, Positively Correlated With Month and Negatively Correlated With Male Breeder Death
Juvenile survival in birds is difficult to estimate but this vital rate can be an important consideration for management decisions. We estimated juvenile survival of cooperatively breeding Florida Scrub-Jays (Aphelocoma coerulescens) in a landscape degraded by fire suppression and fragmentation using data from marked (n = 325) and unmarked juveniles (n = 1,306) with an integrated hierarchical Bayesian model. To assess the combined analyses, we also analyzed these datasets separately, with a Cormack-Jolly-Seber (CJS) model (marked) and young model (unmarked). Our data consisted of monthly censuses of territorial family groups from Florida Scrub-Jay populations in East Central Florida collected over a 22-yr period. Juvenile survival was estimated from July when young Florida Scrub-Jays begin developing independence to March when they become first year individuals and grouped according to the habitat quality class of their natal territory that were based on shrub height (with intermediate shrub heights being optimal and short and tall shrub heights being suboptimal) and the presence of sandy openings (the preferred open having many sandy openings; closed not having enough). Parameter estimates in the combined analysis were intermediate to the separate analyses. Notable differences among the separate analyses were that suboptimal habitat survival was lower in the unmarked analysis, the unmarked analysis showed a linear effect of time not seen in the marked analysis, and there was an effect of male breeder death in the marked but not unmarked analysis. The combined data analysis provided more inference than did either data set analyzed separately including juveniles in optimal-closed territories unexpectedly had higher survival than those in optimal-open, survival increased through time, and male breeder death had a negative effect on survival. This study suggests that optimal-closed habitat may play an important role in juvenile Florida Scrub-Jay survival perhaps by providing better cover from predators and warrants further investigation for management implications.
Florida Scrub-Jay↗
Summary and Annotated Bibliography of Measurement Error Corrections with Potential Application in Future Quesst Mission Community Noise Studies
This document is motivated by likely needs of the Quesst mission community response tests, which will culminate in data collection and estimation of dose-response regression relationships for consideration by domestic and international aviation regulators. Furthermore, basic research questions evaluating interactions between rates of community annoyance, dose levels, and indicators of the presence of rattle, vibration, and startle hinge on hypothesis testing in the context of regression models. For a variety of reasons, noise doses may be known only imprecisely and may not reflect the actual level experienced by responding subjects. These differences between true dose and estimated dose, be they systematic or random, constitute covariate measurement error. Available statistics literature speaks to the impacts of measurement error on regression models, both in terms of bias in estimated coefficients and predicted values, and in terms of the loss of statistical power for hypothesis testing. Given the particulars of a categorical annoyance response variable and a continuous noise dose predictor variable subject to measurement error during testing, the emphasis of this report is on findings and methods pertinent to generalized linear (and mixed) models likely to be employed during the Quesst mission community tests. We reach the following conclusions: 1. Of four reviewed methods, structural Bayesian measurement error models and simulation extrapolation (SIMEX) may be the most readily applicable to Quesst mission community noise study objectives. 2. If warranted, a linear measurement model can help model systematic sources of measurement error that the classical measurement error does not. 3. For its ready implementation and small additional input requirements, simulation extrapolation may be ideally suited for addressing secondary research questions involving interactions between annoyance, noise dose, and other factors through hypothesis testing. 4. For their flexibility and ability to propagate uncertainty, structural Bayesian hierarchical models have great appeal for mission purposes; some care may be needed in developing appropriate probability models describing actual noise exposure during testing. An annotated bibliography logs additional papers and resources that may be of value to analysts in other projects and disciplines.
Dose-Response Model↗
Effects of Topography on Tropical Forest Structure Depend on Climate Context
Topography affects abiotic conditions which can influence the structure, function, and dynamics of ecological communities. An increasing number of studies have demonstrated biological consequences of fine-scale topographic heterogeneity but we have a limited understanding of how We merged high-resolution (1 sq. meter) data on topography and canopy height derived from airborne lidar with ground-based data from 15 forest plots in Puerto Rico distributed along a precipitation gradient spanning ca. 800 to 3,500 mm yr(exp -1). Ground-based data included species composition, estimated above-ground biomass (AGB), and two key functional traits (wood density and leaf mass per area, LMA) that reflect resource-use strategies and a trade-off between hydraulic safety and hydraulic efficiency. We used hierarchical Bayesian models to evaluate how the interaction between topography climate is related to metrics of forest structure (i.e., canopy height and AGB), as well as taxonomic and functional alpha- and beta-diversity. Fine-scale topography (characterized with the topographic wetness index, TWI) significantly affected forest structure and the strength (and in some cases direction) of these effects varied across the precipitation gradient. In all plots, canopy height increased with topographic wetness but the effect was much stronger in dry compared to wet forest plots. In dry forest plots, topographically wetter microsites also had higher levels of AGB but in wet forest plots, topographically drier microsites had higher AGB. Fine-scale topography influenced functional composition but had only weak or non-significant effects on taxonomic and functional alpha- and beta-diversity. For instance, community-weighted wood density followed a similar pattern to AGB across plots. We also found a marginally significant association between variation of wood density and topographic heterogeneity that depended on climate context. Synthesis: The effects of fine-scale topographic heterogeneity on tropical forest structure and composition depend on the climate context. Our study demonstrates how a stronger integration of topographic heterogeneity across precipitation gradients could improve estimates of forest structure and biomass, and may provide insight to the ways that topography might mediate species responses to drought and climate change.
Tropical dry forest↗
Atacama Cosmology Telescope measurements of a large sample of candidates from the Massive and Distant Clusters of WISE Survey: Sunyaev-Zeldovich effect confirmation of MaDCoWS candidates using ACT
Context. Galaxy clusters are an important tool for cosmology, and their detection and characterization are key goals for current and future surveys. Using data from the Wide-field Infrared Survey Explorer (WISE), the Massive and Distant Clusters of WISE Survey (MaDCoWS) located 2839 significant galaxy overdensities at redshifts 0.7 . z . 1.5, which included extensive follow-up imaging from the Spitzer Space Telescope to determine cluster richnesses. Concurrently, the Atacama Cosmology Telescope (ACT) has produced large area millimeter-wave maps in three frequency bands along with a large catalog of Sunyaev-Zeldovich (SZ)-selected clusters as part of its Data Release 5 (DR5). Aims. We aim to verify and characterize MaDCoWS clusters using measurements of, or limits on, their thermal SZ effect signatures. We also use these detections to establish the scaling relation between SZ mass and the MaDCoWS-defined richness. Methods. Using the maps and cluster catalog from DR5, we explore the scaling between SZ mass and cluster richness. We do this by comparing cataloged detections and extracting individual and stacked SZ signals from the MaDCoWS cluster locations. We use complementary radio survey data from the Very Large Array, submillimeter data from Herschel, and ACT 224 GHz data to assess the impact of contaminating sources on the SZ signals from both ACT and MaDCoWS clusters. We use a hierarchical Bayesian model to fit the mass-richness scaling relation, allowing for clusters to be drawn from two populations: one, a Gaussian centered on the mass-richness relation, and the other, a Gaussian centered on zero SZ signal. Results. We find that MaDCoWS clusters have submillimeter contamination that is consistent with a gray-body spectrum, while the ACT clusters are consistent with no submillimeter emission on average. Additionally, the intrinsic radio intensities of ACT clusters are lower than those of MaDCoWS clusters, even when the ACT clusters are restricted to the same redshift range as the MaDCoWS clusters. We find the best-fit ACT SZ mass versus MaDCoWS richness scaling relation has a slope of p1 = 1.84+0.15 −0.14, where the slope is defined as M ∝ λ p1 15 and λ15 is the richness. We also find that the ACT SZ signals for a significant fraction (∼57%) of the MaDCoWS sample can statistically be described as being drawn from a noise-like distribution, indicating that the candidates are possibly dominated by low-mass and unvirialized systems that are below the mass limit of the ACT sample. Further, we note that a large portion of the optically confirmed ACT clusters located in the same volume of the sky as MaDCoWS are not selected by MaDCoWS, indicating that the MaDCoWS sample is not complete with respect to SZ selection. Finally, we find that the radio loud fraction of MaDCoWS clusters increases with richness, while we find no evidence that the submillimeter emission of the MaDCoWS clusters evolves with richness. Conclusions. We conclude that the original MaDCoWS selection function is not well defined and, as such, reiterate the MaDCoWS collaboration’s recommendation that the sample is suited for probing cluster and galaxy evolution, but not cosmological analyses. We find a best-fit mass-richness relation slope that agrees with the published MaDCoWS preliminary results. Additionally, we find that while the approximate level of infill of the ACT and MaDCoWS cluster SZ signals (1–2%) is subdominant to other sources of uncertainty for current generation experiments, characterizing and removing this bias will be critical for next-generation experiments hoping to constrain cluster masses at the sub-percent level.
large↗
Defining Change Thresholds: What Change Is Outside Typical Sources of Variation?
Researchers often have a difficult time defining meaningful thresholds for change. We sometimes identify subtle changes but what amount of change is beyond typical sources of variation? This is especially complicated when trying to understand new disease pathogenesis like the constellation of eye changes leading to Spaceflight-associated Neuro-ocular Syndrome (SANS). To support decision makers in defining minimal meaningful change, we used a Bayesian hierarchical model to estimate innate sources of variability such as natural day to day variation. Healthy subjects were recruited and imaged with MRI, OCT, and US on separate days and measured by several technicians. Models were developed specifying random effects for the sources of variation – between left and right eyes, within-individuals over time, between raters, and finally between individuals. This allowed us to find the posterior distribution for the total typical variation, within an eye, which we use to define a threshold where change beyond typical sources of variation is likely. This threshold is now used as our earliest indicator of systematic increase in Total Retinal Thickness (a precursor to optic disc edema).
Millennia Young↗
Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics
The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.
Statistics↗
Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation
Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.
Bayesian hierarchical modeling↗
Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID-19 in the Conterminous United States
This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.
spatial epidemiology↗
Bayesian Hierarchical Air-Sea Interaction Modeling: Application to the Labrador Sea
The objectives are to: 1) Organize data from 26 MINIMET drifters in the Labrador Sea, including sensor calibration and error checking of ARGOS transmissions. 2) Produce wind direction, barometer, and sea surface temperature time series. In addition, provide data from historical file of 150 SHARP drifters in the Labrador Sea. 3) Work with data interpretation and data-modeling assimilation issues.
Niiler, Pearn P.↗
Detection of Vegetation Trends in Highly Variable Environments After Grazing Exclusion in Mongolia
Aims: Environmental variability (e.g. in precipitation) has a large effect on vegetation dynamics, and this often makes it difficult to assess the recovery of vegetation after a disturbance. In this study, we assessed vegetation recovery trends in response to grazing exclusion while considering for the annual environmental variability. Location: Two regions with highly variable precipitation: a steppe near Mandalgobi, in Mongolia's Central Gobi province, and a desert steppe near Bulgan, in Mongolia's South Gobi province. Methods: Changes in vegetation were observed along grazing gradients at the above two sites, and vegetation thresholds were identified. We established reference plots in pre-threshold areas along the grazing gradients. We removed the impact of livestock grazing at various locations along the grazing gradients by establishing exclosures, and investigated vegetation from 2005 to 2013. We developed a smoothed hierarchical model within a Bayesian framework, and examined the effect of grazing exclusion on vegetation, focusing especially on the extent of grass cover recovery. In addition, we compared soil nutrient conditions in the reference plots and inside and outside each exclosure along the grazing gradients. Results: Temporal trends in the cover of perennial grass in each plot inside and outside of the exclosures largely coincided, irrespective of grazing intensity, and exclosure had no effect (Bulgan) or a negative effect (Mandalgobi) on vegetation recovery. Soil nutrient content was not significantly affected by exclosure at a given distance from the grazing source, but decreased significantly with decreasing grazing intensity. Thus, recovery of the land from a post-threshold state may not be apparent even after 9 yr of grazing exclusion in environments with highly variable precipitation. Conclusion: The effect of exclosure duration on perennial grass cover was limited, even after controlling for environmental variability. Once a vegetation threshold has been crossed, merely removing livestock from the landscape may not be sufficient for that area to recover.
Bayesian statistics;Drought;Environmental variabil↗