2. Basics of direct Monte Carlo - will introduce the basic idea of direct Monte Carlo and how one estimates the error involved.
3. Pseudo-Random Number Generators - general structure of such generators and how one tests them
4. Generating non-uniform random variables - how you can use random numbers that are uniformly distributed on [0, 1] to generate samples of a random variable with either a continuous or discrete distribution.
5. Variance Reduction -
6. Importance Sampling
7. Markov chain background
8. Markov chain Monte Carlo
9. Convergence and error bars for MCMC
10. Optimization
11. Further Topics