Monte Carlo method

E86905

The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic systems.


Statements (49)
Predicate Object
instanceOf computational method
numerical method
simulation technique
stochastic method
aimsAt approximating numerical results
appliedTo Bayesian inference
complex integrals
computer graphics
engineering design
high-dimensional problems
operations research
optimization problems
probabilistic systems
quantitative finance
queueing systems
radiation transport
risk analysis
statistical physics
stochastic processes
associatedWith Los Alamos National Laboratory NERFINISHED
Manhattan Project
basedOn law of large numbers
probability theory
characterizedBy repeated random experiments
statistical estimation of quantities
use of pseudo-random numbers
developedIn 20th century
estimates distribution functions
expectations
integrals
probabilities
variances
hasAdvantage applicability to complex models
dimension-independent convergence rate
hasDisadvantage potentially high computational cost
statistical noise in estimates
hasProperty convergence rate proportional to inverse square root of sample size
includes Markov chain Monte Carlo
Monte Carlo integration
Monte Carlo simulation
importance sampling
quasi-Monte Carlo method
variance reduction techniques
namedAfter Monte Carlo
nameRefersTo Monte Carlo casino in Monaco
notableDeveloper John von Neumann
Nicholas Metropolis
Stanislaw Ulam
uses random sampling

Referenced by (5)
Subject (surface form when different) Predicate
Markov chain Monte Carlo ("Monte Carlo integration")
basedOn
Stanislaw Ulam
coInvented
Master of Financial Engineering ("Monte Carlo simulation")
focusesOn
Monte Carlo method ("Monte Carlo integration")
includes
Georges-Louis Leclerc, Comte de Buffon ("Buffon's needle probability problem")
knownFor

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