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 ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 51, No. 3 (2002), pp. 257-280 (24 pages) We model daily catches of fishing boats in the Grand Bank fishing grounds. We use ...

Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 49, No. 3 (2000), pp. 339-354 (16 pages) The field of archaeology provides a rich source of complex, non-standard problems ...

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent ...

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 ...

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

Interventions targeting drinking water safety and responsible dog management could reduce echinococcosis incidence in China.

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...