Design of experiments has traditionally relied on the frequentist hypothesis testing framework where the optimal size of the experiment is specified as the minimum sample size that guarantees a required level of power. Sample size determination may be performed analytically when the test statistic has a known asymptotic sampling distribution and, therefore, the power function is available in analytic form. Bayesian methods have gained popularity in all stages of discovery, namely, design, analysis and decision making. Bayesian decision procedures rely on posterior summaries whose sampling distributions are commonly estimated via Monte Carlo simulations. In the design of scientific studies, the Bayesian approach incorporates uncertainty about the design value(s) instead of conditioning on a single value of the model parameter(s). Accounting for uncertainties in the design value(s) is particularly critical when the model includes nuisance parameters. In this talk, I present methodology that utilizes the large-sample properties of the posterior distribution together with Bayesian additive regression trees (BART) to efficiently obtain the optimal sample size and decision criteria in fixed and adaptive designs. I introduce a fully Bayesian procedure that incorporates the uncertainty associated with the model parameters including the nuisance parameters at the design stage. The proposed approach significantly reduces the computational burden associated with Bayesian design and enables wide adoption of Bayesian operating characteristics.
17 Aprile 2026, ore 12
Shirin Golchi
Department of Epidemiology and Biostatistics
McGill University, Montreal, Canada
In person: Room V (4th floor) building CU002 Scienze Statistiche
Webinar: https://uniroma1.zoom.us/j/83625004899?pwd=bXCtz0mp759PUh2lkqT0BUoVa0Uegg.1
ID riunione: 836 2500 4899
Passcode: 123456