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Monte Carlo Simulation is also known as Monte Carlo Method. It is a computerized mathematical process that allows people to see all the possible results of their decisions and analyse the impact of risk involved in the process of simulation. This above said processes helps in a variety of decision making situations there by ruling the disadvantage of uncertainty, hence making decisions and outcomes efficient, reliable and useful. In simple words, it is a process of quantitative analysis of data and removal of risk. This process is used by professionals in wider fields such as project management, engineering, research, development, transportation, insurance, environment, finance, energy and manufacturing. It has a variety of uses ranging from day to day life works to complex features such as nuclear bombs and atom bombs. It was first used by a couple of scientists who were working on a nuclear project. It was named after the famous town Monte Carlo, which is famous for its exquisite resorts and wonderful casinos. The Simulation has been used in a number of processes and experiments and has been quite successful in recognition after gaining success in the Second World War. The Detailed study and analysis of Monte Carlo simulation can be studied from Monte Carlo Simulation Assignment Help.
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In Monte Carlo simulation process risk is performed by building models of possible results. Result is calculated many times, each time using different set of random probability function values. This process involves thousands of recalculations before it is completed, depending upon the number of uncertainties and its range. A realistic way of describing uncertainty in variables of a risk analysis is probability distribution.
Common probability distributions are: –
- Normal or Bell Curve:
In this probability distribution the variation about the mean is described by defining the mean and the standard deviation by the user. It is a simple process and values near the mean or in the middle has more chances to occur. Examples include energy prices and inflation rates. It can be well explained by Monte Carlo Simulation Assignment Help.
The values are not symmetric and are positively skewed. Examples include stock prices, real estate property values.
In this type of probability distribution all the values have an equal chance of being selected. The minimum and maximum value is simply defined by the user. Example includes production costs.
In this type of probability distribution the minimum, most likely, and maximum values are defined by the user. The variable which are near to more likely have more chance of being occurred.
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