The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …

Virtual sensing technology is crucial for high product quality and productivity in any industry.
This review aims to clarify the trend of research and application of virtual sensing technology …

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and
exercise psychology research are scarce. In this article, the foundations of Bayesian …

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 …

Current molecular methods of species delimitation are limited by the types of species
delimitation models and scenarios that can be tested. Bayes factors allow for more flexibility …

Determining the fitness of specific microbial genotypes has extensive application in
microbial genetics, evolution, and biotechnology. While estimates from growth curves are …

I evaluated the predictive ability of statistical models obtained by applying seven methods of
variable selection to 12 ecological and environmental data sets. Cross‐validation, involving …

In cancer pharmacology (and many other areas), most dose-response curves are
satisfactorily described by a classical Hill equation (ie 4 parameters logistical). Nevertheless …

Despite its importance to structural equation modeling, model evaluation remains
underdeveloped in the Bayesian SEM framework. Posterior predictive p-values (PPP) and …

Efficient asset management is of paramount importance, particularly for systems with costly
downtime and failure. As in energy and capital-intensive industries, the economic loss of …