Results 201 to 210 of about 38,389 (265)
Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking. [PDF]
Pan S, Zhang L, Bradley JR, Banerjee S.
europepmc +1 more source
ABSTRACT Habitat restoration may protect brown trout (Salmo trutta) populations from loss of genetic diversity by increasing environmental carrying capacity and, consequently, population size. However, the accompanying release of hatchery‐reared fish may threaten the genetic integrity of native populations through introgression.
Noor Alwash +6 more
wiley +1 more source
Temporal Evolution of Contaminant Mass Discharge: Effect of Source Remediation at Contaminated Sites
Abstract Contaminant mass discharge (CMD) is a key metric for evaluating remediation performance at contaminated sites posing a risk to groundwater. This study assesses temporal CMD trends and associated uncertainties using a geostatistical approach at a chlorinated solvent contaminated site following source zone remediation, supported by two decades ...
Anton Bøllingtoft +4 more
wiley +1 more source
Modeling insurance claims using Bayesian nonparametric regression. [PDF]
Shams M, Ghosh K.
europepmc +1 more source
The vast increase in biodiversity data generated through citizen science initiatives, alongside a growing suite of remote sensing products and advanced modelling tools, has opened new avenues for rapidly, accurately and efficiently monitoring species trends to inform conservation, management and policy.
Ramiro D. Crego +7 more
wiley +1 more source
Frequentist and Bayesian Predictive Inference for the Log-Logistic Distribution Under Progressive Type-II Censoring. [PDF]
Zhang Z, Gui W.
europepmc +1 more source
Computational protocol for hierarchical Bayesian modeling of perception and generalization in fear conditioning. [PDF]
Yu K +3 more
europepmc +1 more source
Navigating the mysterious space of evolutionary histories. [PDF]
Drummond AJ, Popinga A.
europepmc +1 more source
Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks. [PDF]
Li D, Li K, Zhou W, Anastasio MA.
europepmc +1 more source

