Week 1 & 2: Frequentist statistics

Lecture 1: Introduction to random variables, common probability distributions
Lecture 2: Basis statistical theory, including Cramer-Rao bound
Lecture 3: Hypothesis testing, Neyman-Pearson lemma, ROC curves
Practical 1: Introduction to R

Week 3 & 4: Bayesian statistics

Lecture 4: Introduction to Bayesian statistics: Bayes theorem, prior choices
Lecture 5: Introduction to Bayesian statistics: Bayesian hypothesis testing, posterior predictive checking, hierarchical models
Lecture 6: Bayesian sampling methods
Practical 2: Introduction to JAGS

Week 5 & 6: Statistics in GW astronomy

Lecture 7: Stochastic processes, optimal filtering, signal-to-noise ratio, sensitivity curves
Lecture 8: Frequentist statistics in GW astronomy – FAR, Fisher Matrix, PSD estimation
Lecture 9: Bayesian statistics in GW astronomy – PE, population inference
Practical 3: GW population analysis

Week 7 & 8: Advanced topics

Lecture 10: Time series analysis – auto-regressive process, moving average processes, ARMA models etc.
Lecture 11: Nonparametric regression – kernel density estimation, smoothing splines, wavelets
Lecture 12: Gaussian processes, Dirichlet processes
Practical 4: Nonparametric curve fitting