# Monte Carlo Simulation Assignment Help

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Monte Carlo Simulation is a mathematical/statistical technique used to account for risk in decision making and quantitative analysis. It used by statisticians in different fields such as project management, finance, manufacturing, energy, research and development, engineering, oil and gas, insurance, and environment. The Monte Carlo simulation provides a wide variety of possible outcomes to the decision maker.It also offers a range of possibilities on which the outcomes will occur for different choices of actions.

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## Learn How Monte Carlo Simulation From Our Experts Work

Monte Carlo simulation analyses risk by developing models of possible results. According to our providers of help with Monte Carlo simulation assignments, it achieves this by substituting different values for all factors that have inherent uncertainties. It then calculates results continuously, using different random values every time. Based on the amount of uncertainties and the different ranges specified for each of these uncertainties, a Monte Carlo simulation may perform thousands of recalculations before it is successfully completed. The final result would be a distribution of possible outcome values. To learn more about how Monte Carlo simulation works, get in touch with our Monte Carlo simulation assignment helpers.

## Common Probability Distributions In Monte Carlo Simulation

There are several probability distributions involved in Monte Carlo simulation. The most common ones as highlighted by our Monte Carlo simulation homework help experts include:

**Normal distribution**: Also known as a bell curve, a normal distribution defines the mean or the expected value as well as the standard deviation to help mathematicians understand the variation about the mean. In a normal distribution, the values near the mean are more likely to occur. Normal distribution is symmetric and is used to describe a variety of natural phenomena. Some of the variables it describes include energy prices and inflation rates.**Lognormal distribution**: Unlike normal distribution, values in a lognormal distribution are positively skewed. Lognormal distributions are used to make a representation of values that do not drop below zero and have unlimited positive potential. Some of the variables described in this distribution include stock prices, oil reserves, and real estate property values.**Uniform distribution**: In a uniform distribution, all values involved have an equal chance of occurrence. The user defines both the maximum and minimum values. Examples of variables that are likely to be uniformly distributed include future sales of new products or manufacturing costs.**Triangular distribution**: In this distribution, the user defines the minimum values, most likely values and the maximum values. The values near the ‘most likely’ have a higher chance of occurrence. The variables described here include past sales over a given period of time and the levels of inventory.**PERT**: Just like in triangular distribution, the user defines the minimum values, the most likely values, and the maximum values. The values that have a higher chance of occurrence are those near the ‘most likely’. PERT distribution is used in project management to measure the direction of the project.**Discrete distribution**: In discrete distribution, the user defines a set of specific values that are likely to occur and the probability of them to occur. A good example of discrete distribution variables include the results of a lawsuit. There is a 40 % chance of settlement, 30 % change of a negative verdict, 20 % chance of a positive verdict, and a 10 % chance of mistrial.

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- Financial derivatives
- Stochastic modeling
- Direct sampling methods
- Error analysis
- Variance reduction
- Markov chain Monte Carlo
- Rare event simulation
- Exact sampling
- Importance of sampling
- Gillespie and related algorithms

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