9th Jun, 2020

Russian Academy of Sciences

顺心彩票【KOK795.COM】是亚洲地区最有影响力之一的游戏平台，顺心彩票官网涵盖各种真人客户端，体育，足球，NBA直播，电子棋牌，电子捕鱼APP！顺心彩票致力于打造亚洲最全的棋牌游戏平台。

Question

Asked 11th Oct, 2018

Hi everyone,

I currently use MCS method to analyze effect of some uncertain parameters on electrical power system and run 10,000 simulations to calculate the output which approximately takes around 1 hour.

I recently read some methods which can reduce the MCS scenarios thus, resulting in low computational time.

So, can our fellow researchers elaborate more on this topic or suggest me any other techniques which has the potential to significantly reduce the computational time of MCS (say around 5 minutes for my work) with reasonable accuracy?

Cheers

Sam

If you have a deal with uncertain parameters then the use of the method of similar trajectories or weighted method of correlated sampling can dramatically decrease the computation time

It depends on what is your application of Monte Carlo simulation. You could have different computational time reduction strategy. But generally speaking, to have a good sampling technique will make your Monte Carlo simulation much easier and more efficient. I would suggest the Latin hypercube sampling (LHS) sampling technique, which I used quite often. It will make the distribution of your samples very close to the expected distribution with small number of sample generation.

3 Recommendations

Sam,

Surrogate models (emulators) are a well established method for reducing the run time in applications like this. Gaussian Processes work particularly well.

10,000 runs of a model in an hour isn’t particularly slow though. Unless you have to do this repeatedly is an hour too long?

1 Recommendation

I think of the following. (Maybe this is already a standard method?)

Suppose you have a nonlinear function F(X) where X is a random variable with a known distribution. With Monte Carlo, an estimate of the expected value of F is the average of a set of values of F(X) based on random drawings of X.

An alternative, non-random, method is as follows. Assume for simplicity that X has a finite range. Divide this range in a finite number of similar adjacent intervals ("bins"), numbered 1 to N. An estimate of the expected value of F is the sum over i=1..N of F(Xi) times Pi, with Xi being the middle of interval i, and Pi being the probability mass of interval i. This might be much more accurate than N random drawings from the distribution of X.

If the range of X is not finite, as with the normal distribution, then cut off the tails with very low probability; for instance cut off outside 4 times the standard deviation at both ends. Of course, the above estimate must then by divided by the sum of the Pi.

This method is of limited value when X is multidimensional, with a large number of dimensions, say M, since the number of "cells" is N to the power M.

1 Recommendation

Dear Gurung,

Read the paper "IMPROVING COMPUTATIONAL EFFICIENCY OF MONTE-CARLO

SIMULATIONS WITH VARIANCE REDUCTION".

Good Luck

2 Recommendations

15th Oct, 2018

I don't know if it might help your case, but I would suggest utilizing the Genetic Algorithm. I had used it in my Master Thesis Research for developing a systematic methodology to design a very complex class of high-frequency radar antennas, and I can say that it is a very powerful algorithm, once you formulate it into your problem effectively. In my case it gave results in 2-3 iterations (generations), maximum 4-5 in the worst start-up scenario. The results in my case came in the order of seconds, and I don't think it would take more than the order of minutes in your case, even if the amount of data points is larger. Good luck!

1 Recommendation

This might seem a bit unrelated, but if you're sampling your Monte Carlo points from some distribution and you're interested in the distribution of outputs (i.e. after simulating each point, or whatever you're doing) then you may be interested in the Koopman Operator (see, e.g., https://engineering.ucsb.edu/~harbabi/research/KoopmanIntro.pdf).

Depending on which direction you're going (i.e. whether you have some known target distribution of outputs, say the performance distribution of the electrical power systems, or some known levels of uncertainties on the inputs for which you want to generate the output distribution) you may also want to checkout the Frobenius-Peron Operator.

1 Recommendation

The term of "Variance Reduction" is very large subject. There is many ways to reduce the variance of simulation. Each unneccessary counting can extend your computational time. On the other hand, it can increase your statistical error rate. So, please carefully read the special variance reduction techniques of your code.

1 Recommendation

It depends on what is your application of Monte Carlo simulation. You could have different computational time reduction strategy. But generally speaking, to have a good sampling technique will make your Monte Carlo simulation much easier and more efficient. I would suggest the Latin hypercube sampling (LHS) sampling technique, which I used quite often. It will make the distribution of your samples very close to the expected distribution with small number of sample generation.

3 Recommendations

You might investigate "weight windows." They have been highly successful in nuclear particle transport applications.

What is the difference between IC50, Ki and Kd of a given inhibitor in an assay?

Question

17 answers

- Asked 4th Oct, 2017

- Jonathan Ayew

Article

Les techniques informatiques de simulation sont essentielles au statisticien. Afin que celui-ci puisse les utiliser en vue de résoudre des problèmes statistiques, il lui faut au préalable développer son intuition et sa capacité à produire lui-même des modèles de simulation. Ce livre adopte donc le point de vue du programmeur pour exposer ces outils...

Article

Thesis (B.S.) in Electrical Engineering--University of Maine, 1923. Includes bibliographical references (leaf 41).

Article

Written for the Dept. of Electrical Engineering. Thesis (Ph.D.). Bibliography: leaves [191]-205.

Get high-quality answers from experts.

小泽玛利亚 樱井步 苍井空 石黑京香 桃谷绘里香 白咲舞 川岛和津实 黑泽爱 樱井步 小泽爱丽丝 七濑茱莉亚 樱朱音 原千寻 若菜奈央 稻森丽奈 尾野真知子 友田真希 花野真衣 雪本芽衣 雨宫真贵 冲田杏梨 高阪保奈美 霞理沙 泽井芽衣 北野望 桃谷绘理香 橋本凉 波多野结衣 仁科百华 柚木提娜 长泽梓 大沢佑香 天海翼 前田香织 前田香织 二宫沙树 铃木里美 希崎杰西卡 麻仓优 麻美由真 原更沙 葵实野理 上原瑞穂 福山沙也加 铃木麻奈美 西野翔 神谷姬 希志爱野 琴乃 希崎杰西卡 秋元里奈 原干惠 杏树纱奈 佐藤遥希 Sato Haruki 前田香织 二宫沙树 仁科百华 樱木凛 秋元里奈 小泽爱丽丝 原纱央莉 浅井舞香 音羽雷恩 天海丽 大泽佑香 百合野もも 里美尤里娅 铃原爱蜜莉 美竹铃 吉沢明步 吉泽明步 早川濑里奈 美竹凉子 松岛枫 佐佐木希 樱井梨花 立花美凉 小泉彩 里美尤里娅 铃原爱蜜莉 美竹铃 松岛枫 佐佐木希 朝美穗香 上原结衣 纹舞兰