Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished
by its use of probability distributions to describe uncertain quantities, which leads to elegant …

Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in
many fields and has been highly valued and strongly supported by academia and industry …

The use of both linear and generalized linear mixed‐effects models (LMM s and GLMM s)
has become popular not only in social and medical sciences, but also in biological sciences …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

A new edition of the definitive guide to logistic regression modeling for health science and
other applications This thoroughly expanded Third Edition provides an easily accessible …

Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

The response of soil carbon dynamics to climate and land‐use change will affect both the
future climate and the quality of ecosystems. Deep soil carbon (> 20 cm) is the primary …

Geographically weighted regression (GWR) was proposed in the geography literature to
allow relationships in a regression model to vary over space. In contrast to traditional linear …

Multilevel Structural equation models are most often estimated from a frequentist framework
via maximum likelihood. However, as shown in this article, frequentist results are not always …