Tentative schedule

Day 1  Big data, reproducibility, multiple hypothesis testing, relation to Bayesian inference.

Day 2  False Discovery Rate (FDR) and the Benjamini-Hochberg procedure.

Day 3  Bayes, and the local FDR.

Day 4 Testing hypotheses on a tree: error rates and controlling strategies.

Day 5  High-probability FDP bounds and multiple confidence intervals.

Day 6  Selective inference.

Day 7   The knockoff filter, linear regression with fixed-X design. The model-X framework.

Day 8   Constructing knockoffs, group knockoffs and multi-resolution analysis.

Day 9   The problem of confounding: conditional testing and causal inference.

Day 10  Conformal inference: trustworthy machine predictions.