Linked from

The 84 pages that link to Monte Carlo method, each with the reason it gives.

• Game of chanceRelated: Simulations can test game odds and payout behavior when direct calculation is difficult.

• Kristen NygaardRelated: Nygaard’s early computing work included Monte Carlo methods before the Simula project.

• Outcome (probability)Related: Each simulated outcome contributes to an estimate.

• Continuous uniform distributionRelated: Uniform random numbers are a standard starting point for simulations.

• Torsten HägerstrandRelated: Hägerstrand used simulation to model the spread of innovations when detailed mechanisms were uncertain.

• Ballistic depositionRelated: Simulations implement random particle arrivals and measure the resulting growth statistics.

• Classical spin modelsRelated: Sampling configurations estimates thermodynamic properties of large spin systems.

• Dittert conjectureRelated: Sampling vertices repeatedly gives numerical estimates of the expected simplex volume.

• General-purpose computing on graphics processing unitsRelated: Independent samples can often be generated and evaluated simultaneously on GPUs.

• Lagrangian particle trackingRelated: Stochastic particle tracking uses random samples to represent unresolved turbulent transport.

• Materials modelingRelated: It samples configurations and thermodynamic states that direct trajectories may miss.

• Molecular modellingRelated: Molecular simulations use sampling moves to explore configurations without following physical time.

• War (card game)Related: Simulations can estimate game length and the frequency of ties.