Variance Reduction Techniques in Monte Carlo Methods

Kleijnen, J.P.C.; Ridder, Ad; Rubinstein, Reuven Y.

doi:10.1007/978-1-4419-1153-7_638

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…Monte Carlo methods are simulation algorithms using to estimate a numerical quantity in a statistical model of a real system and it is practical and accurate way to simulate experiments that would be difficult or impossible to carry out (Kleijnen, Ridder, & Rubinstein, 2010). The computer programs has used to executing it.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of variance reduction techniques in BEAMnrc Monte Carlo simulation to improve the computing efficiency

Mohammed

Chakir

Boukhal

et al. 2016

Journal of Radiation Research and Applied Sciences

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a b s t r a c t The application of Variance Reduction Techniques (VRT) in Monte Carlo (MC) codes brings significant improvement in efficiency and a significant profit in the time of simulation. Monte Carlo radiation transport codes have provided by these techniques to solve the time problematic. BEAMnrc Monte Carlo code has several techniques, that have enabled to be the best and fastest code, Including rang rejection, photon forcing, splitting of photon or electron, (Direction, Selective and Uniform) Bremsstrahlung Photon Splitting and Russian roulette (RR). This paper aims to analysis the variance reduction techniques available in BEAMnrc code, validation BEAMnrc model for simulation a Saturne43 accelerator of 12 MV photon beam, and comparing between MCNPX and BEAMnrc. The results obtained show that employing of Direction Bremsstrahlung Photon Splitting (DBS) technique alone or combined with others techniques lead to enhance the efficiency in BEAMnrc simulation. It improves by 16 times compared with Selective Bremsstrahlung Photon Splitting (SBS), 3.7 times (without RR) and it increases about 29 times (with RR) compared to Uniform Bremsstrahlung Photon Splitting (UBS), and also the efficiency increases by factor of 1130 compared to analogue simulation.A good agreement between measurement and calculation of PDD curves for 10 Â 10 cm 2 of 12 MV photon beam with local differences less than 2% has been obtained.BEAMnrc has an efficiency greater than MCNPX code in case of analogue simulation (without VRTs).

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Section: Introductionmentioning

confidence: 99%

Evaluation of variance reduction techniques in BEAMnrc Monte Carlo simulation to improve the computing efficiency

Mohammed

Chakir

Boukhal

et al. 2016

Journal of Radiation Research and Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The drawback of Monte Carlo integration is its computational cost: Many samplesthousands or even millions-may be required to obtain results of acceptable accuracy. Numerous techniques have been developed to reduce the variance of the Monte Carlo estimator, and hence the number of samples needed (e.g., Kleijnen & Rubinstein, 2013). For our specific application, 200 samples turned out to produce sufficiently accurate results (see Appendix A for implementation details of the Monte Carlo integration).…”

Section: Accepted Articlementioning

confidence: 99%

An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n‐Moment Bulk Microphysics Scheme

Bieli

Dunbar

Jong

et al. 2022

J Adv Model Earth Syst

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Historically, microphysics schemes were tuned to data in an ad-hoc way, resulting in parameter values that are not repeatable or explainable • Bayesian inference puts uncertainty quantification and parameter learning on solid mathematical grounds, but is computationally expensive • We present a proof-of-concept of computationally efficient Bayesian learning applied to a new bulk microphysics scheme called "Cloudy"

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“…Numerous techniques have been developed to reduce the variance of the Monte Carlo estimator, and hence the number of samples needed (e.g., Kleijnen & Rubinstein, 2013).…”

Section: Cloudy Model Descriptionmentioning

confidence: 99%

An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme

Bieli

Dunbar

Jong

et al. 2022

Preprint

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Historically, microphysics schemes were tuned to data in an ad-hoc way, resulting in parameter values that are not repeatable or explainable• Bayesian inference puts uncertainty quantification and parameter learning on solid mathematical grounds, but is computationally expensive• We present a proof-of-concept of computationally efficient Bayesian learning applied to a new bulk microphysics scheme called "Cloudy"

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Variance Reduction Techniques in Monte Carlo Methods

Cited by 15 publications

References 24 publications

Evaluation of variance reduction techniques in BEAMnrc Monte Carlo simulation to improve the computing efficiency

Evaluation of variance reduction techniques in BEAMnrc Monte Carlo simulation to improve the computing efficiency

An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n‐Moment Bulk Microphysics Scheme

An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme

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