Related papers: Bayesian spatial modelling of terrestrial radiatio…
The paper discusses some 3D simulations to compute the ionizing radiation dose during laser-plasma experiments leading to the generation of accelerated protons and electrons. Also, we suggest a new method to increase the measurement…
Most epidemic models are spatially aggregate and the index which is most used for planning and policy numbers, the r number, typically refers to a single system of interest. Even if r numbers are calculated for each of adjacent areas,…
To account for measurement error (ME) in explanatory variables, Bayesian approaches provide a flexible framework, as expert knowledge about unobserved covariates can be incorporated in the prior distributions. However, given the analytic…
Per- and polyfluoroalkyl substances (PFAS) are typically encountered as mixtures of distinct chemicals with distinct effects on multiple health outcomes. Estimating joint causal effects using spatially-dependent observed data is…
We consider the problem of Bayesian density estimation on the positive semiline for possibly unbounded densities. We propose a hierarchical Bayesian estimator based on the gamma mixture prior which can be viewed as a location mixture. We…
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation,…
Intensity mapping experiments survey the spectrum of diffuse line radiation rather than detect individual objects at high signal-to-noise. Spectral maps of unresolved atomic and molecular line radiation contain three-dimensional information…
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations.…
Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area level data on cancer risk factors with high resolution and complete reach, which…
Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on…
An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches…
Compositional regression models with a real-valued response variable can generally be specified as log-contrast models subject to a zero-sum constraint on the model coefficients. This formulation emphasises the relative information conveyed…
This paper studies the relationship between the student's abilities in the second year of high school and the infrastructural endowment in all Italian municipalities, using spatial Bayesian modelling. Municipal student scores are obtained…
Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made using a geostatistical model have uncertainty that typically varies spatially. However, this uncertainty is…
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease…
Quantifying spatial and/or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model, but severe computational bottlenecks arise when spatial…
Femtosecond laser filament-induced plasma spectroscopy (FIPS) demonstrates great potentials in the remote sensing for identifying atmospheric pollutant molecules. Due to the widespread aerosols in atmosphere, the remote detection based on…
Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates…
Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may…
Geostatistical models for multivariate applications such as heavy metal soil contamination work under Gaussian assumptions and may result in underestimated extreme values and misleading risk assessments (Marchant et al, 2011). A more…