Advances in surrogate based modeling, feasibility analysis, and optimization: A review
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
[書籍][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression
Background Estimating the health effects of multi-pollutant mixtures is of increasing interest
in environmental epidemiology. Recently, a new approach for estimating the health effects of …
in environmental epidemiology. Recently, a new approach for estimating the health effects of …
Progression of COVID‐19 from urban to rural areas in the United States: a spatiotemporal analysis of prevalence rates
Purpose There are growing signs that the COVID‐19 virus has started to spread to rural
areas and can impact the rural health care system that is already stretched and lacks …
areas and can impact the rural health care system that is already stretched and lacks …
sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields
Geostatistical data—spatially referenced observations related to some continuous spatial
phenomenon—are ubiquitous in ecology and can reveal ecological processes and inform …
phenomenon—are ubiquitous in ecology and can reveal ecological processes and inform …
A case study competition among methods for analyzing large spatial data
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …
“big data” era, however, has lead to the traditional Gaussian process being computationally …
Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments
JT Thorson - Fisheries Research, 2019 - Elsevier
Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support
interdisplinary fisheries management in the US and worldwide. These assessment activities …
interdisplinary fisheries management in the US and worldwide. These assessment activities …
Bayesian spatial modelling with R-INLA
The principles behind the interface to continuous domain spatial models in the R-INLA
software package for R are described. The integrated nested Laplace approximation (INLA) …
software package for R are described. The integrated nested Laplace approximation (INLA) …
[書籍][B] Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
F Korner-Nievergelt, T Roth, S Von Felten, J Guélat… - 2015 - books.google.com
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines
the Bayesian and frequentist methods of conducting data analyses. The book provides the …
the Bayesian and frequentist methods of conducting data analyses. The book provides the …