Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

This is a preview. Log in through your library . Abstract In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy ...

What are the different types of predictive modeling? Your email has been sent Predictive modeling is a type of data mining that is used in a variety of situations and industries. This process involves ...

There are several books in the extensive and varied literature on Turbulence that deal, in statistical terms and in the context of fluid dynamics, with the phenomenon itself, as well as its many ...

In a recent article published in the eLife Journal, researchers launched a possum excreta surveillance program across 350 km 2 in the Mornington Peninsula near South Melbourne, Australia. The study ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

Researchers have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to ...

Researchers have developed a new statistical model that predicts which cities are more likely to become infectious disease hotspots, based both on interconnectivity between cities and the idea that ...

Nutrient loading has been linked to many issues including eutrophication, harmful algal blooms, and decreases in aquatic species diversity. In order to develop mitigation strategies to control ...