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Nicholas Metropolis helped develop electronic-computer implementations of the Monte Carlo method at Los Alamos. Instead of tracking every possible sequence of events analytically, the method samples individual random histories and estimates quantities from their collective outcomes.

His firsthand 1987 account describes simulations of neutron transport: sampled flight distances and interactions generate a history, and repeating histories estimates transport behaviour. The scientific contribution connects probability to a computational experiment, making problems accessible even when their many possible paths are difficult to enumerate.^[ref]

Stanisław Ulam helped develop the modern computational use of Monte Carlo methods. The firsthand account by Nicholas Metropolis describes Ulam's proposal to revive statistical sampling with electronic computers and its subsequent development with John von Neumann.^[ref]

In particle-transport calculations, one samples successive events—scattering, absorption and branching—to produce many possible histories. Averaging their outcomes estimates a macroscopic quantity. The link between a physical random process, probability distributions and an executable algorithm made difficult calculations accessible without enumerating every possible trajectory.^[ref]