Monte Carlo methods and Markov Chain algorithms have long been central to computational science, forming the backbone of numerical simulation in a variety of disciplines. These techniques employ ...

Monte Carlo simulation is a mathematical technique for considering the effect of uncertainty on investing as well as many other activities. A Monte Carlo simulation shows a large number and variety of ...

Monte Carlo methods have become a cornerstone in nuclear systems analysis, particularly for sensitivity studies, which determine how variations in nuclear data can affect key reactor parameters. These ...

A technique that provides approximate solutions to problems expressed mathematically. Using random numbers and trial and error, it repeatedly calculates the equations to arrive at a solution. Many of ...

Recently, estimating ratios of normalizing constants has played an important role in Bayesian computations. Applications of estimating ratios of normalizing constants arise in many aspects of Bayesian ...

There are two flavors of QMC, (a) variational Monte Carlo (VMC) and (b) projector Monte Carlo (PMC). VMC starts by proposing a functional form for the wavefunction and then optimizes the parameters of ...

With highly specialized instruments, we can see materials on the nanoscale – but we can’t see what many of them do. That limits researchers’ ability to develop new therapeutics and new technologies ...

This is a preview. Log in through your library . Abstract We consider Monte Carlo methods for the classical nonlinear filtering problem. The first method is based on a backward pathwise filtering ...

You're currently following this author! Want to unfollow? Unsubscribe via the link in your email. Follow Andy Kiersz Every time Andy publishes a story, you’ll get ...

If you enjoyed this article, I’d like to ask for your support. Scientific American has served as an advocate for science and industry for 180 years, and right now may be the most critical moment in ...