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gptkbp:instanceOf
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gptkb:simulation_method
|
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gptkbp:advantage
|
computationally intensive
flexible modeling
handles complex systems
results are approximate
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gptkbp:alsoKnownAs
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gptkb:Monte_Carlo_method
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gptkbp:application
|
gptkb:radiation_therapy
gptkb:machine_learning
option pricing
queueing theory
project management
financial modeling
statistical physics
particle transport
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gptkbp:basedOn
|
random sampling
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gptkbp:category
|
gptkb:probability_theory
gptkb:simulation
computational mathematics
numerical analysis
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gptkbp:namedAfter
|
gptkb:Monte_Carlo,_Monaco
|
|
gptkbp:notableFigure
|
gptkb:John_von_Neumann
gptkb:Nicholas_Metropolis
gptkb:Stanislaw_Ulam
|
|
gptkbp:originatedIn
|
gptkb:Los_Alamos_National_Laboratory
1940s
|
|
gptkbp:output
|
distributions of outcomes
probabilistic estimates
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|
gptkbp:purpose
|
numerical integration
optimization
risk analysis
uncertainty quantification
|
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gptkbp:relatedTo
|
gptkb:Markov_Chain_Monte_Carlo
gptkb:Quasi-Monte_Carlo_method
stochastic simulation
|
|
gptkbp:requires
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gptkb:generator
|
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gptkbp:step
|
aggregate results
define a domain of possible inputs
generate random inputs
perform deterministic computation
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|
gptkbp:usedIn
|
gptkb:mathematics
computer science
engineering
finance
physics
statistics
|
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gptkbp:bfsParent
|
gptkb:Measuring_Market_Risk
gptkb:Derivatives_Instruments
|
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gptkbp:bfsLayer
|
7
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|
https://www.w3.org/2000/01/rdf-schema#label
|
Monte Carlo Simulation
|