The Monte Carlo simulation estimates the probability of different outcomes in a process that cannot easily be predicted because of the potential for random variables.

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 ...

The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov ...

Marking a significant step in the roadmap for quantum advantage for financial applications, Goldman Sachs and QC Ware researchers have designed new, robust quantum algorithms that outperform ...

A quantum version of a computer algorithm widely used in finance, engineering and scientific modelling shows promising signs of operating much faster than existing methods. Experts say there are many ...