Empirical Bayesian methods occupy a unique position at the interface of frequentist and Bayesian paradigms by estimating prior distributions directly from observed data. This approach preserves the ...

A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

Bayesian trials formally integrate prior evidence with accruing data to yield posterior probabilities, supporting interim learning, adaptive modifications, and direct predictive futility/efficacy ...