Lecture
This lecture covers the properties and error estimates of the Monte Carlo method in stochastic simulation. It explains how to set up algorithms for stochastic models when the distribution is unknown, and how to simulate it. The goal is to compute the sample mean using the Monte Carlo estimator and generate independent realizations. The lecture also discusses unbiased projections and confidence intervals, emphasizing the strong law of large numbers. It concludes with the adaptation of the Monte Carlo algorithm and the sequential Monte Carlo method.