Priors are important for achieving proper posteriors with physically meaningful covariance structures Gaussian random fields (GRFs) since the likelihood typically only provides limited information about structure under in-fill asymptotics. We extend recent penalized complexity prior framework and develop a principled joint range marginal variance of one-dimensional, two-dimensional, three-dimensional Matérn GRFs fixed smoothness. The is weakly informative penalizes by shrinking toward...

Gaussian random fields (GRFs) play an important part in spatial modelling, but can be computationally infeasible for general covariance structures.An efficient approach is to specify GRFs via stochastic partial differential equations (SPDEs) and derive Markov field (GMRF) approximations of the solutions.We consider construction a class non-stationary with varying local anisotropy, where anisotropy introduced by allowing coefficients SPDE vary position.This done using form diffusion equation...

Around 70 Mha of land cover changes (LCCs) occurred in Europe from 1992 to 2015. Despite LCCs being an important driver regional climate variations, their temperature effects at a continental scale have not yet been assessed. Here, we integrate maps historical with model investigate air and humidity effects. We find average change -0.12 ± 0.20 °C, widespread cooling (up -1.0 °C) western central summer spring. At scale, the mean is mainly correlated agriculture abandonment (cropland-to-forest...

Objective Wasting and stunting may occur together at the individual child level; however, their shared geographic distribution correlates remain unexplored. Understanding separate inform interventions. We aimed to assess spatial codistribution of wasting, underweight investigate among children aged 6–59 months in Somalia. Setting Cross-sectional nutritional assessments surveys were conducted using structured interviews communities Somalia biannually from 2007 2010. A two-stage cluster...

Methods for detecting contemporary, fine-scale population genetic structure in continuous populations are scarce. Yet such methods vital ecological and conservation studies, particularly under a changing landscape. Here we present novel, spatially explicit method that call landscape relatedness (LandRel). With this method, aim to detect is sensitive spatial temporal changes the We interpolate determined values based on SNP genotypes across Interpolations calculated using Bayesian inference...

Accurate estimates of the under-five mortality rate in a developing world context are key barometer health nation. This paper describes new model to analyze survey data on this context. We interested both spatial and temporal description, that is wishing estimate across regions years investigate association between spatially varying covariate surfaces. illustrate methodology by producing yearly for subnational areas Kenya over period 1980–2014 using from Demographic Health Surveys, which use...

Variance parameters in additive models are typically assigned independent priors that do not account for model structure. We present a new framework prior selection based on hierarchical decomposition of the total variance along tree structure to individual components. For each split tree, an analyst may be ignorant or have sound intuition how attribute branches. In former case Dirichlet is appropriate use, while latter penalised complexity (PC) provides robust shrinkage. A bottom-up...

Abstract The need for rigorous and timely health demographic summaries has provided the impetus an explosion in geographic studies low- middle-income countries. Many of these present fine-scale pixel-level maps attempt to answer needs current era precision public health. However, even though household surveys with a two-stage cluster design stratified by region urbanicity are major source data, cavalier approaches taken acknowledging survey design. We investigate extent which accounting...

The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, development of efficient ocean sampling approaches is increasingly important for understanding the processes. Currents, winds, freshwater runoff make variables such as salinity very heterogeneous, standard statistical models can be unreasonable describing complex environments. We employ a class Gaussian Markov random fields that learns...

Isotropic covariance structures can be unreasonable for phenomena in three-dimensional spaces. In the ocean, variability of a response may vary with depth, and ocean currents lead to spatially varying anisotropy. We construct class non-stationary anisotropic Gaussian random fields (GRFs) three dimensions through stochastic partial differential equations (SPDEs), where computations are done efficiently using Markov field approximations. A key novelty is parametrization anisotropy vector...

Modern climate models pose an ever-increasing storage burden to computational facilities, and the upcoming generation of global simulations from next Intergovernmental Panel on Climate Change will require a substantial share budget research centers worldwide be allocated just for this task. A statistical model can used as means mitigate by providing stochastic approximation simulations. Indeed, if suitably validated formulated draw realizations whose spatiotemporal structure is similar that...

Spatial aggregation with respect to a population distribution involves estimating aggregate quantities based on observations from individuals. In this context, geostatistical workflow must account for three major sources of error: weights, fine scale variation, and finite variation. However, these error are commonly ignored, the instead treated as fixed density surface. We improve common practice by introducing sampling frame model allowing models simply transparently. This preserves point...

A non-stationary spatial Gaussian random field (GRF) is described as the solution of an inhomogeneous stochastic partial differential equation (SPDE), where covariance structure GRF controlled by coefficients in SPDE. This allows for a flexible way to vary structure, intuition about resulting can be gained from local behaviour equation. Additionally, computations done with computationally convenient Markov fields which approximate true GRFs. The model applied dataset annual precipitation...

Abstract We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization can be visualized as tree. edges tree represent ratios variances, for example broad-sense heritability, are quantities EK natural exist. Penalized complexity priors...