
Explore Bayesian inference techniques for tumor growth modeling, focusing on parameter identification and censored data analysis in mathematical oncology research.

Master the fundamentals of effective business experimentation, from hypothesis formation and metric selection to experiment design, group splitting, and results analysis for data-driven decision making.