Class 07 Monte Carlo simulation

Class 07 Monte Carlo simulation - Notes on Monte Carlo...

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10/06/09 ENGR 2600 MAU 1 Notes on Monte Carlo Simulation in Minitab Prof. Tom Willemain © 2005 Thomas R. Willemain
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10/06/09 ENGR 2600 MAU 2 Monte Carlo Simulation Where possible, we prefer an exact analytical solution using probability theory to estimate the probability of an event. However, the axioms and propositions of probability theory can be difficult to apply in complicated problems. Where necessary, we resort to a Monte Carlo solution. Monte Carlo simulation creates an experiment whose outcome is the event of interest. Each replication of the experiment gives a random estimate of the probability: the event either does or does not occur. By repeating the experiment many times and averaging the results, we can estimate the correct answer to the probability problem. Also, by keeping track of the variability in the estimate from one replication to the next, we can assess the uncertainty in the estimate of the answer.
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10/06/09 ENGR 2600 MAU 3 Dice Simulation in Minitab Generate two columns of random numbers equally likely to be any of {1, 2, 3, 4, 5, 6}. Each row constitutes an experiment. Sum the two columns. Each row of the sum column is an event computed from the elementary events of the experiment. Tabulate the results in the sum column.
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10/06/09 ENGR 2600 MAU 4 Dice Simulation in Minitab This part mimics the throwing of 2 fair dice. This part forms the sum of the two die values.
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10/06/09 ENGR 2600 MAU 5 Dice Simulation in Minitab The Monte Carlo results approximate the true results.
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10/06/09 ENGR 2600 MAU 6 Monte Carlo Exercise #1 A product manager must develop a working prototype in one month, or else! She has 10 engineers to assign to the task.
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Class 07 Monte Carlo simulation - Notes on Monte Carlo...

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