Bayesian Methods for Biomedical Data Science

Develop Bayesian analyses of biological data from prior specification and posterior estimation to computational diagnostics and multimodal integration. Practical work covers conjugate and non-conjugate models, Gibbs and Hamiltonian Monte Carlo, convergence checks, hierarchical regression, regularisation and variational inference. Later sessions compare data-fusion strategies and Bayesian factor models while quantifying uncertainty in integrated biomedical datasets.

Sessions: 30 November–4 December, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between meetings.

Live online training; fee €480. For advanced MSc, doctoral and postdoctoral researchers with R/Bioconductor, probability, statistics and regression experience.