Probabilistic Models

Syllabus

• Applied probability topics beyond the core lectures, typically presented via student-led talks and exercises.
• Extreme value analysis; record distributions; probabilistic classification ideas.
• Random number generators and simulation basics.
• Markov chain Monte Carlo (MCMC) methods (intro).
• Mathematical models of gambling and risk.
• Psychometrics and survey/polling models.
• Networks and random graph models.
• Probability paradoxes and concentration phenomena.
• Simulation studies and computational experiments.