Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen.

It tells you not only what could happen, but how likely it is to happen. An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the entire range of distribution functions. The advent of spreadsheet applications for personal computers provided an opportunity for professionals to use Monte Carlo simulation in everyday analysis work. First introduced for Lotus for DOS in , RISK has a long-established reputation for computational accuracy, modeling flexibility, and ease of use. The introduction of Microsoft Project led to another logical application of Monte Carlo simulation—analyzing the uncertainties and risks inherent to the management of large projects.

### Introduction of Risk Management

RISK is also used for project management. What is Monte Carlo Simulation? How Monte Carlo Simulation Works Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty.

Lognormal Values are positively skewed, not symmetric like a normal distribution. Uniform All values have an equal chance of occurring, and the user simply defines the minimum and maximum. Triangular The user defines the minimum, most likely, and maximum values. PERT The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Discrete The user defines specific values that may occur and the likelihood of each. Results show not only what could happen, but how likely each outcome is.

Graphical Results. This is important for communicating findings to other stakeholders.

Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most.

## Monte Carlo Simulation

Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis. Correlation of Inputs. Monte Carlo Simulation with Palisade The advent of spreadsheet applications for personal computers provided an opportunity for professionals to use Monte Carlo simulation in everyday analysis work.

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See our User Agreement and Privacy Policy. See our Privacy Policy and User Agreement for details. Published on Nov 26, Some problems in risk analysis cannot be expressed in an analytical form; others are difficult to define in a deterministic manner. Monte Carlo methods also known as stochastic simulation techniques consist of running "numerical experiments" to observe what happens over a large number of runs of a stochastic model. They consist of using repeated random sampling from input probability distributions, execution of the model with these stochastic inputs, then aggregation of the large number of executions to obtain an estimate of the quantity of interest.

These methods rely on the ability of computers to generate pseudo-random numbers from various relevant probability distributions. SlideShare Explore Search You.

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## The Monte Carlo Simulation Method for System Reliability and Risk Analysis

We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime. Monte Carlo methods for risk analysis. Upcoming SlideShare. Like this presentation? Why not share! Statistics and risk modelling using Embed Size px. Start on. Show related SlideShares at end.

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