Ví dụ về việc sử dụng Monte carlo simulation trong Tiếng anh và bản dịch của chúng sang Tiếng việt
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What is a Monte Carlo simulation?
A popular tool to risk analysis is the Monte Carlo simulation.
What are Monte Carlo simulations?
Best way to seed mt19937_64 for Monte Carlo simulations.
What is Monte Carlo Simulation Technique?
This problem demonstrates some strengths and weaknesses of Monte Carlo simulation.
So, what is Monte Carlo simulation?
You will need to buy an add-on ora software program to run the Monte Carlo simulation.
What is the Monte Carlo simulation?
In the next five chapters, you will see examples ofhow you can use Excel to perform Monte Carlo simulations.
Exactly what is Monte Carlo simulation?
Monte Carlo simulation enables us to model situations that present uncertainty and then play them out on a computer thousands of times.
For example, we approximate p-values by running Monte Carlo simulations, which reinforces the meaning of the p-value.
A Monte Carlo simulation using Solex 12 with 1000 clones of the asteroid shows that the asteroid's possible positions overlap Earth.[8].
The computer goes through, essentially, a Monte Carlo simulation, and it looks at all the possible routes available,” Moss says.
Yrs ago I asked the same general question, and by crushing that single bottleneck,reduced the execution time of a Monte Carlo simulation from 21 hours to 7 hours.
Many companies use monte carlo simulation as an important part of their decision-making process.
Although the knowledge domain does of course encompass much more, the most involved techniques even mentioned in the entirePMBOK® Guide are probability distributions and Monte Carlo simulation.
Financial planners use Monte Carlo simulation to determine optimal investment strategies for their clients' retirement.
In Applied Statistical Modelling,you will deal with many popular techniques such as Markov Chains and Monte Carlo Simulation with an opportuniuty to apply these techniques to a real data set.
In particular, Monte Carlo simulations show that one will get a very high R squared, very high individual t-statistic and a low Durbin- Watson statistic.
Using data from NASA's LRO on the sizes and temperatures of cold traps,Haley put together a probabilistic analysis called a Monte Carlo simulation to determine how much carbon dioxide would make it to a cold trap.
This process is called Monte Carlo simulation because generating random numbers is reminiscent of casino games(and Monte Carlo is famous for its casino).
These include basic methods such as partial budgeting, cost-benefit analysis and decision analysis, but also more advanced methodologies such as linear programming,the Markov chain and Monte Carlo simulation.
When the data is in place you can use sophisticated Monte Carlo simulations that project how much work you can accomplish by a given date.
We can use a Monte Carlo simulation to compute the p-value, which is the probability of seeing a chi-square statistic as high as the observed value under the null hypothesis.
If you want to go places in data science though, take some time to get up to speed in a few basic statistical methods, like linear regression, Bayes Theorem and probability, inferential statistics,ordinary least squares regression, Monte Carlo simulations, and time series analysis.
Use a Monte Carlo simulation to estimate the probability that any player in major league baseball will have a hitting streak of 57 or more games in the next century.
Note: The name Monte Carlo simulation comes from the computer simulations performed during the 1930s and 1940s to estimate the probability that the chain reaction needed for an atom bomb to detonate would work successfully.