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gptkbp:instanceOf
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gptkb:mathematical_concept
computational algorithm
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gptkbp:advantage
|
handles high-dimensional problems
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gptkbp:application
|
gptkb:machine_learning
computational biology
engineering
game theory
project management
risk analysis
quantitative finance
statistical physics
radiation transport
|
|
gptkbp:basedOn
|
random sampling
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|
gptkbp:characteristic
|
approximates solutions
uses randomness
|
|
gptkbp:field
|
gptkb:mathematics
computer science
finance
physics
statistics
|
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gptkbp:firstUsedFor
|
neutron diffusion calculations
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Monte Carlo Method
|
|
gptkbp:limitation
|
results are approximate
convergence can be slow
|
|
gptkbp:nameOrigin
|
Monte Carlo Casino
|
|
gptkbp:notableFigure
|
gptkb:John_von_Neumann
gptkb:Stanislaw_Ulam
|
|
gptkbp:notablePublication
|
gptkb:The_Monte_Carlo_Method_(Metropolis_&_Ulam,_1949)
|
|
gptkbp:originatedIn
|
1940s
|
|
gptkbp:relatedTo
|
gptkb:Markov_Chain_Monte_Carlo
gptkb:Quasi-Monte_Carlo_method
|
|
gptkbp:requires
|
generator
|
|
gptkbp:usedFor
|
gptkb:simulation
numerical integration
optimization
probability estimation
|
|
gptkbp:bfsParent
|
gptkb:MCM
gptkb:Simulated_Annealing
|
|
gptkbp:bfsLayer
|
6
|