by M. B. Giles, Sylvestre Burgos
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Suggested foundations for monte carlo and numerical simulation and optimisation and numerical methods. Specialist prerequisites vary by paper.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Turn a discounted payoff into an estimator.
Study dependence and alternatives to independent sampling.
Understand identifiability and fitted model parameters.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.