The key operation in Bayesian inference is to compute high-dimensional integrals. An old
approximate technique is the Laplace method or approximation, which dates back to Pierre …

Background: Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical
developmental windows have been implicated in the etiologies of a wide array of adverse …

Popular frameworks for studying habitat selection include resource‐selection functions
(RSFs) and step‐selection functions (SSFs), estimated using logistic and conditional logistic …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting
a broad class of Bayesian regression models. No samples of the posterior marginal …

Regression dilution is a statistical inference bias that causes underestimation of the strength
of dependency between two variables when the predictors are error‐prone proxies (EPPs) …

The use of complex statistical models has recently increased substantially in the context of
species distribution behavior. This complexity has made the inferential and predictive …

Many studies document negative inbreeding effects on individuals, and conservation efforts
to preserve rare species routinely employ strategies to reduce inbreeding. Despite this, there …

Since the 1970s, the magnitude of turtle cold-stun strandings have increased dramatically
within the northwestern Atlantic. Here, we examine oceanic, atmospheric, and biological …