Search

The growing frequency and size of wildfires across the US necessitates accurate quantitative assessment of evolving wildfire behavior to predict risk from future extreme wildfires. We build a joint model of wildfire counts and burned areas, regressing key model parameters on climate and demographic covariates. We use extended generalized Pareto distributions to model the full distribution of burned areas, capturing both moderate and extreme sizes, while leveraging extreme value theory to focus particularly on the right tail. We model wildfire counts with a zero‐inflated negative binomial model, and join the wildfire counts and burned areas sub‐models using a temporally‐varying shared random effect. Our model successfully captures the trends of wildfire counts and burned areas. By investigating the predictive power of different sets of covariates, we find that fire indices are better predictors of wildfire burned area behavior than individual climate covariates, whereas climate covariates are influential drivers of wildfire occurrence behavior. [ABSTRACT FROM AUTHOR]

Due to the short wavelength of millimeter-wave, active electrical scanning millimeter-wave imaging system requires large imaging scenarios and high resolutions in practical applications. These requirements lead to a large uniform array size and high complexity of the feed network that satisfies the Nyquist sampling theorem. Accordingly, the system faces contradictions among imaging accuracy, imaging speed, and system cost. To this end, a novel, Credible Bayesian Inference of near-field Sparse Array Synthesis (CBI-SAS) algorithm is proposed under the framework of sparse Bayesian learning. The algorithm optimizes the complex-valued excitation weights based on Bayesian inference in a sparse manner. Therefore, it obtains the full statistical posterior Probability Density Function (PDF) of these weights. This enables the algorithm to utilize higher-order statistical information to obtain the optimal values, confidence intervals, and confidence levels of the excitation weights. In Bayesian inference, to achieve a small number of array elements to synthesize the desired beam orientation pattern, a heavy-tailed Laplace sparse prior is introduced to the excitation weights. However, considering that the prior probability model is not conjugated with the reference pattern data probability, the prior model is encoded in a hierarchical Bayesian manner so that the full posterior distribution can be represented in closed-form solutions. To avoid the high-dimensional integral in the full posterior distribution, a variational Bayesian expectation maximization method is employed to calculate the posterior PDF of the excitation weights, enabling reliable Bayesian inference. Simulation results show that compared with conventional sparse array synthesis algorithms, the proposed algorithm achieves lower element sparsity, a smaller normalized mean square error, and higher accuracy for matching the desired directional pattern. In addition, based on the measured raw data from near-field 1D electrical scanning and 2D plane electrical scanning, an improved 3D time domain algorithm is applied for 3D image reconstruction. Results verify that the proposed CBI-SAS algorithm can guarantee imaging results and reduce the complexity of the system.

Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions between space and time. A Bayesian approach employing a hierarchical structure serves as a solution for spatial model inference, addressing the identifiability problem often encountered when utilizing classical approaches like the maximum likelihood method. However, the hierarchical Bayesian approach faces a significant challenge in determining the hyperprior distribution for the variance components of hierarchical Bayesian spatiotemporal models. Commonly used distributions include logGamma for log inverse variance, Half-Cauchy, Penalized Complexity, and Uniform distribution for hyperparameter standard deviation. While the logGamma approach is relatively straightforward with faster computing times, it is highly sensitive to changes in hyperparameter values, specifically scale and shape. This research aims to identify the most optimal hyperprior distribution and its parameters under various conditions of spatial and temporal autocorrelation, as well as observation units, through a Monte Carlo study. Real data on dengue cases in West Java are utilized alongside simulation results. The findings indicate that, across different conditions, the Uniform hyperprior distribution proves to be the optimal choice. [ABSTRACT FROM AUTHOR]

Bayesian analysis was applied to small area models with overdispersed response variables. The benefits of implementing this strategy by Markov Chain Monte Carlo methods make inference straightforward and computationally feasible. In this paper, we apply the strategy into area-level modeling to predict the under-five mortality rate at the district level in Java Island, the most populated region in Indonesia. The result shows that the zero-inflated negative binomial model yields the reduced relative standard error and relative mean squared error when compared to district estimates, the zeroinflated generalized Poisson and Poisson models. [ABSTRACT FROM AUTHOR]

Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets. [ABSTRACT FROM AUTHOR]

Tropical forests are the most species‐rich biomes in the world but suffer high rates of logging and conversion. Tropical tree‐dwelling (arboreal and semi‐arboreal) mesomammals reliant on old‐growth forest structures are especially vulnerable. The degree of behavioral arboreality of semi‐arboreal mammals can be related to forest structure and perceived terrestrial threats. Paired arboreal and terrestrial camera traps are a promising new method for estimating the arboreality of cryptic and nocturnal species. Our study aimed to (1) model the effects of forest structure and anthropogenic disturbance on the detection and occurrence of arboreal and semi‐arboreal mesomammals and (2) evaluate differences in occurrence and detection between paired arboreal and terrestrial camera trap sites for semi‐arboreal mammals while estimating the degree of arboreality. We set 20 terrestrial and arboreal camera trap pairs in eastern Cat Tien National Park (Nam Cat Tien), Vietnam, from June 2019 to September 2020. We evaluated the effects of forest structure and proximity to roads on nine arboreal mesomammal species using single‐season occupancy models. We used multi‐scale occupancy modeling to estimate the degree of arboreality for four semi‐arboreal mammals. All models were fit using hierarchical Bayesian modeling and compared using WAIC. We detected most arboreal and terrestrial mesomammal species currently known to inhabit Nam Cat Tien, including rare and cryptic species. Canopy connectivity and other mature forest characteristics were important for explaining the detection and occurrence of highly arboreal species, while the effect of a tree and focal limb characteristics on detection was species‐specific. All semi‐arboreal species had a greater probability of terrestrial station use than arboreal, suggesting a greater vulnerability to terrestrial threats, though the degree of arboreality varied by species. Using one sampling method underestimated occupancy for most semi‐arboreal species. Multi‐method sampling designs with multi‐scale occupancy modeling can improve estimates of species distribution and habitat use for guiding management and conservation decisions. [ABSTRACT FROM AUTHOR]

Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions between space and time. A Bayesian approach employing a hierarchical structure serves as a solution for spatial model inference, addressing the identifiability problem often encountered when utilizing classical approaches like the maximum likelihood method. However, the hierarchical Bayesian approach faces a significant challenge in determining the hyperprior distribution for the variance components of hierarchical Bayesian spatiotemporal models. Commonly used distributions include logGamma for log inverse variance, Half-Cauchy, Penalized Complexity, and Uniform distribution for hyperparameter standard deviation. While the logGamma approach is relatively straightforward with faster computing times, it is highly sensitive to changes in hyperparameter values, specifically scale and shape. This research aims to identify the most optimal hyperprior distribution and its parameters under various conditions of spatial and temporal autocorrelation, as well as observation units, through a Monte Carlo study. Real data on dengue cases in West Java are utilized alongside simulation results. The findings indicate that, across different conditions, the Uniform hyperprior distribution proves to be the optimal choice.

Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets.

This study examines the latent influence of spatial locations on the relative risks of crash injuries associated with distracted driving (DD) and identifies regions of excess risks for policy intervention. Using a sample of aggregated injury and fatal DD crash records for the period 2015–2019 across 1,024 census block groups in Central Ohio (i.e., the Columbus Metropolitan Area) in the United States, we investigate the role of latent effects along with several covariates such as land-use mix, sociodemographic features, and the built environment. To this end, we specifically leverage a full Bayesian hierarchical formulation with conditional autoregressive priors to account for uncertainty (i.e., spatially structured random effects) stemming from adjacent census block groups. Furthermore, we consider uncorrelated random effects from upper-level administrative units within which each block group is nested (i.e., census tracts and counties). Our analysis reveals that (1) addressing spatial correlation improves the model's performance, (2) block-group-level variability substantially explains the residual random fluctuation, and (3) intersection density appears negatively associated with the relative risks of crash injuries, while more diversified land use can increase injury risk. Based on these findings, we present spatial clusters with twice the relative risks compared to other block groups, suggesting that policies be devised to mitigate severe injuries due to DD and therefore enhance public health.

In this paper, we discuss about the hierarchical Bayesian (HB) estimation concerning system reliability for a waste‐to‐energy (WTE) process. The main goal of this approach is to obtain this estimation WTE process reliability under different loss functions related series, parallel, and k‐out‐of‐m systems. In this case, we can drive system outcome by estimating each individual component. It is assumed that components are independent and identically distributed exponential random variables. Properties of the HB estimations under different loss functions are also provided, and comparisons are made between Bayesian and HB estimators via Monte Carlo simulation. The implementation of the proposed procedure was illustrated in detail by employing numerical practical examples extracted from a WTE process at the final part of this paper. [ABSTRACT FROM AUTHOR]

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R2 > 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g., <50). This methodology (i.e., multi-platform LiDAR data and the hierarchical Bayesian method) provides a potential solution for non-destructive individual-tree AGB modeling with small sample size and high accuracy in both forestry and remote sensing communities. [ABSTRACT FROM AUTHOR]

As world populations age along with speedy internationalization, forecasting mortality for multiple countries or populations with similar socio-economic conditions or close cultural connections has become essential. We apply the hierarchical Bayesian theory to the random walk with drift model governing the dynamics of the logarithm of central death rates for each age and population. Using the mortality data for both genders of three developed countries for an age span 25–84 and a series of fitting age-year windows, and further extending the data set to include both genders of twenty countries, we conclude that the proposed hierarchical Bayesian framework can more accurately capture the mortality trends and overall outperforms the Lee–Carter model and its three extensions in mortality forecasting. Grouping both genders of the twenty countries in three ways, we find that the expected improvement rates per year in the logarithm of central death rate for all twenty countries converge to about 2% except for the US. [ABSTRACT FROM AUTHOR]

Child undernutrition is one of the 10 most significant public health problems worldwide. There is a rapidly growing demand to produce reliable estimates at the micro administrative level with small sample sizes. In this research, the authors employed small area estimation techniques to estimate the prevalence of malnutrition at the zonal level among children under five in Ethiopia. The small area estimation concept was sought for by linking the most recent possible survey data and census data in Ethiopia. The results show that there is spatial variation of stunting, wasting and being underweight across the zone level, showing different locations facing different challenges or different extents. [ABSTRACT FROM AUTHOR]

Individual and joint variations of extreme temperature and precipitation are assessed across Canada using the large ensemble of Canadian Regional Climate Model simulations (CanRCM4-LE) and two corresponding multivariate bias-corrected datasets (Canadian Large Ensembles Adjusted Datasets, CanLEAD-E & S). The overall performance of the three 50-member ensembles is evaluated against the NRCANmet gridded observation for 1951–2000. A hierarchical Bayesian framework is then applied to analyze the biases of each ensemble member, characterize the ensemble uncertainties associated with internal climate variability, and assess the trends and the joint distributions of temperature and precipitation. Further, projected changes of extreme climate indices are assessed across the three ensembles in the historical period and four future scenarios corresponding to +1.5 °C to +4.0 °C warming above the pre-industrial level of 1850–1900. Results show that the CanLEAD products have lower warm and wet biases compared to CanRCM4-LE over most regions and in all seasons except in winter. CanLEAD-S significantly reduces precipitation biases and represents the behaviour of extreme climate indices better than the other two ensembles. All ensembles consistently project strong warming and wetting trends over most parts of southern Canada excluding the Canadian Prairies in summer, which shows a drying trend towards the end of the 21st century. The ensembles show increases in hot extremes in central and southeastern Canada and increases in wet extremes in western coastal regions. The results of the study suggest that CanLEAD-E&S can be used as reliable products for climate change impact assessments at regional scales particularly for the analysis of nonstationary compound events.

In this case study, we demonstrate the use of multilevel process monitoring in quality control. Using high school data, we answer three research questions related to high school student progress during an academic year. The questions are (1) What determines student performance? (2) How can statistical process monitoring be used in monitoring student progress? (3) What method can be used for predictive monitoring of student results? To answer these questions, we worked together with a Dutch high school and combined hierarchical Bayesian modeling with statistical and predictive monitoring procedures. The results give a clear blueprint for student progress monitoring. [ABSTRACT FROM AUTHOR]

Tropical biodiversity is threatened globally by anthropogenic disturbances, particularly forest degradation and overhunting. Where large mammals have been extirpated, smaller bodied "mesomammals" may play an important ecological role (e.g., as seed dispersers). However, these species are disproportionally affected by overhunting for wildlife trade markets and are routinely understudied as they tend to be rare, cryptic, and nocturnal. Few studies have examined spatiotemporal responses to anthropogenic disturbance by mesomammals at the community level, which may identify imbalances within an ecosystem that could threaten species persistence. We deployed camera traps throughout Cat Tien National Park (i.e., Nam Cat Tien area), southern Vietnam, to (1) identify long‐term changes in terrestrial mesomammal richness and (2) evaluate the effects of forest structure and anthropogenic disturbance on an 18‐species mesomammal community within a historically disturbed tropical forest using hierarchical Bayesian community occupancy models. We found that site occupancy was driven by the interaction between distance to seasonally inundated grassland and absolute forest cover (basal area per hectare). This may be due to the combination of intact forest benefits (refuge from predators and hunters, denning sites) and early successional grassland resources (forage quality), as well as high levels of tolerance for disturbed forest among the largely generalist mesomammal community of Nam Cat Tien. We found no negative effects of current anthropogenic factors at the community level. However, we did find that four disturbance‐tolerant small carnivores have been extirpated since the 1990s and continued human presence in the park suggests that hunting and snaring remain an acute threat to native mesomammals. Without continued efforts to address the unsustainable harvest of wildlife, Southeast Asian's remaining mesomammals are at risk of extirpation despite resilience to moderate levels of disturbance. [ABSTRACT FROM AUTHOR]

Abstract Tropical biodiversity is threatened globally by anthropogenic disturbances, particularly forest degradation and overhunting. Where large mammals have been extirpated, smaller bodied “mesomammals” may play an important ecological role (e.g., as seed dispersers). However, these species are disproportionally affected by overhunting for wildlife trade markets and are routinely understudied as they tend to be rare, cryptic, and nocturnal. Few studies have examined spatiotemporal responses to anthropogenic disturbance by mesomammals at the community level, which may identify imbalances within an ecosystem that could threaten species persistence. We deployed camera traps throughout Cat Tien National Park (i.e., Nam Cat Tien area), southern Vietnam, to (1) identify long‐term changes in terrestrial mesomammal richness and (2) evaluate the effects of forest structure and anthropogenic disturbance on an 18‐species mesomammal community within a historically disturbed tropical forest using hierarchical Bayesian community occupancy models. We found that site occupancy was driven by the interaction between distance to seasonally inundated grassland and absolute forest cover (basal area per hectare). This may be due to the combination of intact forest benefits (refuge from predators and hunters, denning sites) and early successional grassland resources (forage quality), as well as high levels of tolerance for disturbed forest among the largely generalist mesomammal community of Nam Cat Tien. We found no negative effects of current anthropogenic factors at the community level. However, we did find that four disturbance‐tolerant small carnivores have been extirpated since the 1990s and continued human presence in the park suggests that hunting and snaring remain an acute threat to native mesomammals. Without continued efforts to address the unsustainable harvest of wildlife, Southeast Asian's remaining mesomammals are at risk of extirpation despite resilience to moderate levels of disturbance.

In recent years, Bayesian approach using Gaussian model as a patch prior has achieved great success in image denoising. However, this approach is not stable in solving inverse problems beyond denoising. A hierarchical Bayesian-based Gaussian mixture model is proposed to model image patch. Using the prior knowledge of the model parameters, the probability distribution of the mean and covariance matrices are modelled by the Gaussian-Wishart distribution，which makes the patch estimation process more stable. Based on the coherence of neighboring patches, the set of similar patches in the window can be derived by the multivariate Gaussian probability distribution of specific mean and covariance. The similarity is measured by the L2-norm metric, which is accelerated by using the summed square image and fast Fourier transform. The aggregation weights are based on the Gaussian distribution similarity with Mahalanobis distance, which are combined with the Gaussian similarity of the spatial domain on the image. The statistical characteristics of the natural image are better fitted. The experimental results for solving image restoration problem demonstrate the capabilities of the proposed method.

The Ethiopian Rift is a unique natural environment to study the different stages of evolution from initial continental rifting to embryonic seafloor spreading. We used transdimensional hierarchical Bayesian seismic ambient noise tomography to first construct group velocity maps across the Afar, Main Ethiopian Rift, and the adjoining plateaus, and then inverted these for a shear wave velocity model. The uppermost mantle shear wave velocity ranges between 3.9 and 4.3 km/s, 5–15% lower than the upper mantle velocity in the PREM model. The combined effect of temperature and partial melt is needed to explain a 15% shear wave velocity reduction in the uppermost mantle. Tectonic and magmatic activities are not limited to the rift center, but instead are widespread within the upper crust beneath the Main Ethiopian Rift and Afar. The Main Ethiopian Rift is dominated by two velocity belts, the Wonji Fault Belt along the rift axis and the Silti-Debre Zeit Fault Zone on the western side of the Central Main Ethiopian Rift; the Boru-Toru structural high appears to serve as a transfer zone between them, exhibiting relatively high crustal velocities (3.6 km/s) at 14 km depth. Low velocities persist in the crust beneath the rift flanks and border faults, indicating that they are still tectonically and magmatically active. The crust beneath the western plateau is characterized by a low-velocity anomaly, implying that the plateau is also active. Low-velocity linear belts are further imaged beneath the western and eastern plateaus, away from the active rift axes. These off-axis belts could represent failed or buried rifts. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R2 > 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g.,

Abstract Determining which factors are most effective for mitigative strategies in conservation management can be difficult for species with complex life cycles. Salmon (Oncorhynchus spp.) migrating through a hydroelectric power system experience conditions that can affect their survival directly within a life stage and indirectly in subsequent life stages through carryover effects. We quantified the association of covariates with survival across life stages of Chinook salmon (O. tshawytscha): juveniles migrating downstream, juveniles in the estuary, subadults in the ocean, and adults migrating upstream. We applied a hierarchical Bayesian mark–recapture model to ~400,000 wild Snake River Chinook salmon (Pacific Northwest, USA). Modeled covariates of survival included migration timing, river temperature, flow, percent of water spilled over dams, snow water equivalent, juvenile fish transportation, juvenile fish length, dam powerhouse passage, sea surface temperature, and the North Pacific Gyre Oscillation. By analyzing these covariates in a unified model, we evaluated their relationships to survival within and across life stages in a common currency. Among direct effects, the negative relationship of sea surface temperature to ocean survival had the largest marginal effect size. Carryover effects also had substantial marginal effect sizes. Ocean survival was associated positively with juvenile length and snow water equivalent and negatively with river temperature. In comparison, direct effects on juvenile and adult survival generally showed smaller marginal effect sizes. Both juvenile and adult survival were negatively associated with river temperature. Juvenile survival increased with fish length, while adult survival was positively related to flow and negatively to percent water spilled at dams. The greatest variability in survival, however, was in the ocean stage. Thus, as the ocean continues to warm and as the human population exerts more pressure on the ecosystem, carryover effects will be increasingly important for recovering salmon and other migratory species.

Determining which factors are most effective for mitigative strategies in conservation management can be difficult for species with complex life cycles. Salmon (Oncorhynchus spp.) migrating through a hydroelectric power system experience conditions that can affect their survival directly within a life stage and indirectly in subsequent life stages through carryover effects. We quantified the association of covariates with survival across life stages of Chinook salmon (O. tshawytscha): juveniles migrating downstream, juveniles in the estuary, subadults in the ocean, and adults migrating upstream. We applied a hierarchical Bayesian mark–recapture model to ~400,000 wild Snake River Chinook salmon (Pacific Northwest, USA). Modeled covariates of survival included migration timing, river temperature, flow, percent of water spilled over dams, snow water equivalent, juvenile fish transportation, juvenile fish length, dam powerhouse passage, sea surface temperature, and the North Pacific Gyre Oscillation. By analyzing these covariates in a unified model, we evaluated their relationships to survival within and across life stages in a common currency. Among direct effects, the negative relationship of sea surface temperature to ocean survival had the largest marginal effect size. Carryover effects also had substantial marginal effect sizes. Ocean survival was associated positively with juvenile length and snow water equivalent and negatively with river temperature. In comparison, direct effects on juvenile and adult survival generally showed smaller marginal effect sizes. Both juvenile and adult survival were negatively associated with river temperature. Juvenile survival increased with fish length, while adult survival was positively related to flow and negatively to percent water spilled at dams. The greatest variability in survival, however, was in the ocean stage. Thus, as the ocean continues to warm and as the human population exerts more pressure on the ecosystem, carryover effects will be increasingly important for recovering salmon and other migratory species. [ABSTRACT FROM AUTHOR]

Understanding the effect of stage‐specific traits on species feeding habits can reveal how natural selection shapes life strategies. Amino acid (AA) nitrogen stable isotopes (δ15N) provide multiple proxies of habitat baseline values and diet that can improve our understanding of species feeding strategies relative to their animal metabolism. We evaluated the effect of body length as a proxy for life stage and sex on the feeding habits of the common dolphin Delphinus delphis delphis using δ13C and δ15N in bulk tissue and AAs δ15N from skin samples collected for almost two decades.For bulk δ13C and δ15N data, we used SIBER analysis to compare isotopic niches by sex and life stage. For AA δ15N data, we developed a hierarchical Bayesian model (HBM) to estimate indices of trophic status (Δ15N and trophic position). The model reflected the natural hierarchical structure of AA data by partitioning variability into three sources: between laboratory replicates, within dolphins and among dolphins.Estimates of Δ15N based on all trophic and source AAs were more precise for each dolphin, less variable among dolphins and on average 2.4‰ higher than indices based on single trophic (Glx) and source (Phe) AAs. Precision was further increased when information was shared among individuals through random effects or regression models. Estimates of trophic position showed similar patterns. Both Δ15N and δ15Nbulk isotopic niches showed no difference by sex, suggesting that males and females have similar feeding habits and may not segregate. However, lower Δ15N values for weaning calves and smaller juveniles discriminate them from adults, whereas δ15N bulk isotopic niches do not. A trophic discrimination factor (TDFTro‐Src) of 3.1‰ was required for reasonable estimates of trophic position for these dolphins.Together, the lack of δ15N differences between sexes, low variation between juveniles and adults and knowledge of common dolphins' social organization support intraspecific feeding cooperation as an important strategy to feed in the highly dynamic marine environment. Our study also presents an efficient way to analyse complex AA δ15N data using HBM to investigate foraging behaviour in long‐lived marine species difficult to study in the wild. [ABSTRACT FROM AUTHOR]

Objective: In this study, we examined the effect of accounting variables and characteristics of peer firms on investment management strategy and we suppose that firm’s financial strategies are dependent to financial strategies of peer firms. Also, financial strategies of peer firms are dependent to their accounting variables and characteristics of peer firms. Therefore there is a relation between investment management, accounting variables and characteristics pbt. Method: In order to carry out the investigation on the hierarchical model and Markov chain Monte Carlo simulation is used. The sample of this research is listed companies in Tehran Stock Exchange during the years 2009-2015. Accounting variables are included the structure of assets and average of the companies' profitability. It also characteristics of peer firms are included the average of stock return and expected growth rate. Results: The results show that the average of the peer firms' expected growth rate has a significant relationship with investment management strategy. Also the result shows that Student's t-distribution has the minimum Standard deviation.

Child undernutrition is one of the 10 most significant public health problems worldwide. There is a rapidly growing demand to produce reliable estimates at the micro administrative level with small sample sizes. In this research, the authors employed small area estimation techniques to estimate the prevalence of malnutrition at the zonal level among children under five in Ethiopia. The small area estimation concept was sought for by linking the most recent possible survey data and census data in Ethiopia. The results show that there is spatial variation of stunting, wasting and being underweight across the zone level, showing different locations facing different challenges or different extents.

Understanding the interconnections between performance outcomes in a system is increasingly important for integrated management. Structural equation models (SEMs) are a type of multiple-variable modeling strategy that allows investigation of directionality in the association between outcome variables, thereby providing insight into their interconnections as putative causal links defining a functional network. A key assumption underlying SEMs is that of a homogeneous network structure, whereby the structural coefficients defining functional links are assumed homogeneous and impervious to environmental conditions or management factors. This assumption seems questionable as systems are regularly subjected to explicit interventions to optimize the necessary trade-offs between outcomes. Using a Bayesian approach, we propose methodological extensions to hierarchical SEMs that accommodate structural heterogeneity by explicitly specifying structural coefficients as functions of systematic and non-systematic sources of variation. We validate the inferential properties of our proposed approach using a simulation study and show that networks can be consistently identified as homogeneous or heterogeneous. We apply the proposed methodological extensions to a dataset from a designed experiment in swine production consisting of six interrelated reproductive performance outcomes to explore physiological links that differed by parity, while accounting for data architecture due to experimental design. Overall, our results indicate that explicit hierarchical SEM-based modeling of heterogeneous functional networks can be used to advance understanding of complex systems in animal production agriculture. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

An autoregressive moving average (ARMA) is a time series model that is applied in everyday life for pattern recognition and forecasting. The ARMA model contains a noise which is assumed to have a specific distribution. The noise is often considered to have a Gaussian distribution. However in applications, the noise is sometimes found that does not have a Gaussian distribution. The first objective is to develop the ARMA model in which noise has a Laplacian distribution. The second objective is to estimate the parameters of the ARMA model. The ARMA model parameters include ARMA model orders, ARMA model coefficients, and noise variance. The parameter estimation of the ARMA model is carried out in the Bayesian framework. In the Bayesian framework, the ARMA model parameters are treated as a variable that has a prior distribution. The prior distribution for the ARMA model parameters is combined with the likelihood function for the data to get the posterior distribution for the parameter. The posterior distribution for parameters has a complex form so that the Bayes estimator cannot be determined analytically. The reversible jump Markov chain Monte Carlo (MCMC) algorithm was adopted to determine the Bayes estimator. The first result, the ARMA model can be developed by assuming Laplacian distribution noise. The second result, the performance of the algorithm was tested using simulation studies. The simulation shows that the reversible jump MCMC algorithm can estimate the parameters of the ARMA model correctly.

We propose a latent topic model with a Markov transition for process data, which consists of time‐stamped events recorded in a log file. Such data are becoming more widely available in computer‐based educational assessment with complex problem‐solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward‐backward variational expectation‐maximization (FB‐VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem‐solving item in the 2012 version of the Programme for International Student Assessment (PISA). [ABSTRACT FROM AUTHOR]

The goal of elucidating the primary mechanisms constraining the assembly and distribution of biodiversity remains among the central unresolved challenges facing the field of ecology. Simulation studies and experimental manipulations have focused on how patterns in community assembly result from bivariate relationships along productivity or environmental gradients. However, the joint influence of multiple resource gradients on the distribution of species richness in natural communities remains understudied. Using data from a large network of multiscale vegetation plots across forests and woodlands of the southeastern United States, we find significant evidence for the scale‐dependent, joint constraints of forest structure and soil resources on the distribution of vascular plant species richness. In addition to their significant partial effects on species richness, understory light levels and soil fertility positively interact, suggesting a trade‐off between the two limiting resources with species richness peaking both in high‐light, low‐fertility conditions as well as low‐light, high‐fertility settings. This finding provides a novel perspective on the biodiversity–productivity relationship that suggests a transition in limiting resources from soil nutrients to light availability when enhanced productivity results in reduced light resources for subordinate individuals. Results likewise have meaningful implications for our understanding of scale‐dependent community assembly processes as size‐asymmetric competition replaces environmental filtering as the primary assembly mechanism structuring temperate forest communities along an increasing soil fertility gradient. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we consider the statistical analysis of parallel system with inverse Weibull distributed components. Due to cost and time constraints, the causes of system failures are masked and the type‐II censored observations might occur in the collected data. Under the symmetric and asymmetric loss functions, the expected Bayesian (E‐Bayesian) method and the hierarchical Bayesian method are proposed to estimate the parameters, as well as the reliability function. Numerical simulations using the Monte Carlo (MC) method are given to demonstrate the performances of the estimations under different masking levels and effective sample sizes. Finally, one data set is analyzed for illustrative purpose. [ABSTRACT FROM AUTHOR]

Anomaly is deviation from common rules. In finance; it can be defined as a pattern in the average of stock returns that is not consistent with the prevailing asset pricing models literature. For anomaly investigation, two common methods are used: portfolio approach and individual firm approach. The aim of this study is to examine accounting anomalies of Fama and French three-factor model at the individual firm level due to the weaknesses of the portfolio approach. The sample consists of 1150 firm – year (13800 firm-month) observations in Tehran Stock Exchange in the period of 2008-2017. Bayesian approach and standard Markov chain Monte Carlo simulation have been used to test hypotheses. The results of the research show that size, book value to market value, profitability, asset growth, working capital accrual items, investments, net stock issuance and external financing, cannot be interpreted as anomaly for Fama and French three factors model when the test is at the individual firm level [ABSTRACT FROM AUTHOR]

Despite considerable progress in hydrological modeling, challenges remain in the interpretation and accurate transfer of hydrological information across watersheds and scales. In the conterminous United States (CONUS), these limitations are related to spatial inconsistencies and constraints in hydrological model structures, including a lack of spatially explicit process components (streams, reservoirs, and watershed development) and restricted estimation of model parameters across watersheds. Collectively, such limitations can impede identification of the causes of streamflow variations across the diversity of watershed sizes and land uses in the CONUS and contribute to model imprecision and spatial inconsistencies in prediction uncertainties. We addressed these concerns with a new approach, the first hybrid (statistical‐mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long‐term mean annual streamflow, applied across diverse environmental settings of the CONUS. The hybrid model coupled previous catchment‐scale (1 km) water balance predictions of "natural" unit area runoff, which are inclusive of major water cycling processes, with additional explanatory variables (e.g., soils, vegetation, land use, topography, water losses in streams, and reservoirs) that account for the effects of natural and cultural water supply and demand processes that operate over large spatial scales and explain streamflow variability across CONUS river basins. Accounting for these statistically unique effects, including a nonlinear surface area‐dependent scaling of water loss in river networks, significantly improved the accuracy of mean streamflow predictions in CONUS basins. Our hybrid modeling approach provides new methods for transferring hydrological information to ungauged locations in river networks, especially those in larger and more culturally diverse CONUS watersheds. Key Points: Integration of water balance methods with a spatially explicit statistical model improves accuracy of streamflow prediction across the USAStudy identifies large‐scale natural and human controls on water delivery from the land to river networks and quantifies in‐stream lossesApproach improves understanding and parameterization of large‐scale hydrological processes and scaling properties to support prediction [ABSTRACT FROM AUTHOR]

The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models. Key Points: Use of hierarchical Bayesian methods with a spatially explicit statistical model improves accuracy of streamflow prediction across the United StatesThe study identifies regionally varying model coefficients for natural and human controls on water transport and losses in watershedsHierarchical models show promise for reducing uncertainties in transfers of hydrological information from gauged to ungauged watersheds [ABSTRACT FROM AUTHOR]

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition. • A novel Bayesian hierarchical spectral regression analysis method is proposed. • The proposed HB-SR significantly improves EEG emotion recognition performance. • The interpretability of the proposed HB-SR is further revealed in current work. [ABSTRACT FROM AUTHOR]

A naval hovercraft was outfitted with a distributed fiber optic sensing system (FOSS) on its cargo deck in which two fibers were placed along orthogonal axes. A series of trucks were loaded onto the cargo deck to simulate operational loading conditions and the resulting strain profiles were measured by the distributed FOSS. The aim of this study is to quantify the deck deflections that correspond to the measured strains under different vehicular loads. This is accomplished by idealizing the cargo deck as a rectangular plate and using the data to estimate the associated plate model parameters. In order to account for measurement noise and model prediction error, as well as the inherent variability of inferences made with different data sets, a hierarchical Bayesian scheme that considers both the model parameter uncertainty and the prediction error variance is utilized to derive the posterior distributions of the model hyper-parameters, i.e. , their mean and variance. Given the volume of FOSS strain data available, different approaches were taken for incorporating the data into the hierarchical approach in order to determine whether the inclusion of all available strain data meaningfully reduced the uncertainty compared to using only time-averaged strain. Comparisons between the different approaches are explored in the context of 95% confidence intervals for strain profiles, deck deflection envelopes, and reliability indices based on allowable deflections. • Strains measured on cargo deck of hovercraft via fiber optic sensing systems. • Model of craft cargo deck calibrated via Bayesian methods used recorded strain data. • Three different hierarchical Bayesian approaches to data incorporation explored. • Hierarchical Bayesian approaches quantify uncertainty in model-predicted deflection. [ABSTRACT FROM AUTHOR]

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L−1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies. • Cyanobacteria blooms were successfully forecast in satellite-resolved lakes. • Overall prediction accuracy was 90% with 88% sensitivity and 91% specificity. • Weekly forecasts were demonstrated for 2192 larger U.S. lakes and reservoirs. • A Bayesian spatiotemporal model was applied to forecast alert level exceedance. • Timely forecasting of cyano-blooms gives managers time to react. [ABSTRACT FROM AUTHOR]

The Korean government is planning an R&D project to convert existing small diesel trucks for parcel delivery services to diesel-electric hybrid trucks as part of its effort to reduce emissions of greenhouse gases and particulate matter (PM). This project started from the realization that parcel delivery trucks have become one of the main sources of PM emissions especially as the parcel delivery service industry has rapidly grown in Korea amid inefficient driving environment for delivery trucks. To evaluate the marketability and feasibility of the project, this study analyzed diesel truck owners' preferences for converting to hybrid vehicles. Hierarchical Bayesian choice-based conjoint analysis was employed to forecast conversion probabilities and willingness to pay for various vehicle attributes, and the influencing demographic factors were then analyzed. The demand forecasting results are expected to enable decision makers to set a reasonable budget for hybrid conversion. For policy implications, the findings suggest that truck owners should be allowed to pay for the conversion in installments, not in a lump sum, and it would be strategically effective to target the consumer segment that prioritizes fuel cost savings relative to other vehicle attributes to promote hybrid conversion. [ABSTRACT FROM AUTHOR]

Rationale: Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. Objectives: We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). Methods: We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. Results: Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. Conclusions: Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD. [ABSTRACT FROM AUTHOR]

Identifying where, when, and how many animals live and die over time is principal to understanding factors that influence population dynamics. Capture–recapture–recovery (CRR) models are widely used to estimate animal survival and, in many cases, quantify specific causes of mortality (e.g., harvest, predation, starvation). However, the restrictive CRR framework can inhibit the consideration and inclusion of some types of recovery data. We developed an extension to the CRR framework to allow for the incorporation of recoveries from indeterminate temporal or spatial origin. This model jointly estimates cause-specific mortality and survival probabilities across multiple spatial and temporal scales, while accounting for differences in mortality-specific reporting and recovery rates. We fitted the model to data on a group of juvenile steelhead trout (Oncorhynchus mykiss) marked with passive integrated transponder tags in the Columbia River basin, USA. Following tagging and release, fish were detected alive at up to six downstream locations and/or recovered dead on one of nine bird colonies during seaward migration. We estimated that, in aggregate, avian predators consumed 31% of juvenile steelhead during outmigration to the ocean (95% CRI: [27, 36]). Colony-specific predation rates ranged from < 1 to 14% among river reaches, with avian predation accounting for > 95% of all steelhead mortality within some reaches. This integrated modelling approach provides a flexible framework to integrate multiple recapture and recovery data sources, providing a more holistic understanding of animal life history, including direct comparisons of cause-specific mortality factors and the cumulative impact of multiple mortality factors across time or space. [ABSTRACT FROM AUTHOR]

The belief-bias effect is one of the most-studied biases in reasoning. A recent study of the phenomenon using the signal detection theory (SDT) model called into question all theoretical accounts of belief bias by demonstrating that belief-based differences in the ability to discriminate between valid and invalid syllogisms may be an artifact stemming from the use of inappropriate linear measurement models such as analysis of variance (Dube et al., Psychological Review, 117(3), 831-863, 2010). The discrepancy between Dube et al.’s, Psychological Review, 117(3), 831-863 (2010) results and the previous three decades of work, together with former’s methodological criticisms suggests the need to revisit earlier results, this time collecting confidence-rating responses. Using a hierarchical Bayesian meta-analysis, we reanalyzed a corpus of 22 confidence-rating studies (N = 993). The results indicated that extensive replications using confidence-rating data are unnecessary as the observed receiver operating characteristic functions are not systematically asymmetric. These results were subsequently corroborated by a novel experimental design based on SDT’s generalized area theorem. Although the meta-analysis confirms that believability does not influence discriminability unconditionally, it also confirmed previous results that factors such as individual differences mediate the effect. The main point is that data from previous and future studies can be safely analyzed using appropriate hierarchical methods that do not require confidence ratings. More generally, our results set a new standard for analyzing data and evaluating theories in reasoning. Important methodological and theoretical considerations for future work on belief bias and related domains are discussed. [ABSTRACT FROM AUTHOR]