Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

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 ...

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social ...

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 ...

This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations ...

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 authors used a Bayesian modeling framework to fit behavior and serotonin neuron activity to reward history across multiple timescales. A key goal was to distinguish value coding from other ...