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 ...

Using an advanced Monte Carlo method, Caltech researchers found a way to tame the infinite complexity of Feynman diagrams and solve the long-standing polaron problem, unlocking deeper understanding of ...

In this special guest feature, Vladimir Kuchkanov, Pricing Solution Architect at Competera, examines how data scientists often forget about classics while good old algorithms are still relevant and ...

Monte Carlo methods have emerged as a pivotal tool in reactor kinetics analysis, offering a robust statistical framework for simulating the stochastic behaviour of neutron transport and interaction ...

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 ...

Particle physicists are building innovative machine-learning algorithms to enhance Monte Carlo simulations with the power of AI. Originally developed nearly a century ago by physicists studying ...

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 ...

Worst-case scenario simulations ensure manufacturing is prepared for all contingencies, but over-sizing or under-sizing may ensue. This results in larger than necessary filters and columns that may ...

It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genomewide association studies, a joint null hypothesis of no genetic effect is tested for ...