This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue ...

Brazilian Journal of Probability and Statistics, Vol. 27, No. 1 (February 2013), pp. 1-19 (19 pages) We introduce a Bayesian analysis for beta generalized distributions and related exponentiated ...

This is a preview. Log in through your library . Abstract A major problem with the Bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...