Variance reduction is a technique used in Monte Carlo methods to improve the precision of simulation results. By reducing the variance associated with each output random variable, the simulation becomes more statistically efficient and produces smaller confidence intervals. Various techniques such as common random numbers, antithetic variates, control variates, importance sampling, stratified sampling, moment matching, conditional Monte Carlo, quasi random variables, subset simulation, and line sampling can be employed for this purpose.

Carnegie Mellon University

Fall 2020

This is an intensive course on computer graphics, covering a variety of topics such as rendering, animation, and imaging. It requires previous knowledge in vector calculus, linear algebra, and C/C++ programming. Concepts include ray tracing, radiometry, and geometric optics, among others.

No concepts data

+ 24 more conceptsCarnegie Mellon University

Spring 2022

Similar to Course ID 29, this course provides a comprehensive introduction to computer graphics. It also demands a strong mathematical and programming background. The topics covered include rasterization, geometric transformations, and Monte Carlo ray tracing.

No concepts data

+ 22 more concepts