For years, the artificial intelligence industry has followed a simple, brutal rule: bigger is better. We trained models on massive datasets, increased the number of parameters, and threw immense ...

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

The Covid-19 pandemic reminded us that everyday life is full of interdependencies. The data models and logic for tracking the progress of the pandemic, understanding its spread in the population, ...

At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Forbes contributors publish independent expert analyses and insights. The “exciting” things programmers like is making things happen — deep down, every programmer out there got their start because ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

A Covid-19 restrictions sign hangs outside a supermarket in Austin, Texas. Lauren Ancel Meyers at the University of Texas at Austin has shared her team’s modeling results with city officials who make ...