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

Research on income risk typically treats its proxy—income volatility, the expected magnitude of income changes—as if it were unchanged for an individual over time, the same for everyone at a point in ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...

A Bayesian method for outlier-robust estimation of multinomial choice models is presented. The method can be used for both correlated as well as uncorrelated choice alternatives and guarantees ...

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...

One case often looks very different from the next, and it is precisely this complexity and behavioral variability that makes finding insider threats so tricky. Insider threat actors can cause harm to ...

Thomas Stopka is an associate professor and epidemiologist with the Department of Public Health and Community Medicine at the Tufts University School of Medicine. In his NIH-funded interdisciplinary ...

Bayesian networks are powerful tools in probabilistic reasoning, allowing us to model complex systems where uncertainty and causal relationships intertwine. At their core, Bayesian Networks are ...

The coral reef ecosystems of the Maldives are critical to the nation’s ecological integrity, economic development, and ...