Monte Carlo Crash Course: Quasi-Monte Carlo
Monte Carlo Crash Course Quasi-Monte Carlo We’ve learned how to define and apply Monte Carlo integration—fundamentally, it’s the only tool we need. In the remaining chapters, we’ll explore ways to reduce variance and successfully sample difficult distributions. Variance & Correlation In chapter two, we determined that the variance of a Monte Carlo estimator is inversely proportional to its sample count. Empirically, we confirmed that our integrators’ expected error scaled with $$\frac{1}{\sq